Abstract
In CRYPTO 2019, Chen et al. showed how to construct pseudorandom functions (PRFs) from random permutations (RPs), and they gave one beyond-birthday secure construction from sum of Even-Mansour, namely in the single-key setting. In this paper, we improve their work by proving the multi-key security of , and further tweaking but still preserving beyond birthday bound (BBB) security. Furthermore, we use only one random permutation to construct parallelizable and succinct beyond-birthday secure PRFs in the multi-key setting, and then tweak this new construction. Moreover, with a slight modification of our constructions of tweakable PRFs, two parallelizable nonce based MACs for variable length messages are obtained.
1. Introduction
Random numbers are widely used in engineering practice. In particular, randomization is central to cryptography. One can generate random numbers by using physical random sources such as chaos-based [] and quantum-based [] random number generator. However, obtaining random numbers from physical phenomena requires high quality of the entropy source, and is also device-dependent so that the corresponding cost is not cheap. Besides, in some cryptographic applications, the way of generating random numbers above is not friendly due to its uncontrollability. Motivated by cryptographic applications, Blum and Micali [] and Yao [] formalized the modern notation of pseudorandom generators from the perspectives in computational complexity. Later, Goldreich et al. [] proposed the concept of pseudorandom functions (PRFs). Informally, is said to be a PRF where K is a uniformly random string with enough entropy, if for any input x, can be computed efficiently and can not be distinguished from a truly random value. PRFs are important in cryptography with fruitful applications in encryption, identification, and authentication.
In theory, PRFs can be obtained from one-way functions [,], but this general transformation is not practical. Some other algebraic constructions, such as number theory-based [,] or lattice-based PRFs [,,], are still inefficient. Therefore, it is significant to construct PRFs from symmetric primitives both in theory and practice. There are a series of works to build the PRFs from pseudorandom permutations (PRPs)/block ciphers [,,]. Recently, Chen et al. [] proposed a method to construct PRFs from random permutations (RPs). In [], the construction (which means sum of one-round Even-Mansour based on two independent permutations) was proved beyond-birthday secure in the single-key setting.
About , there are three questions we may ask: (i) Is beyond-birthday secure in the multi-key setting? (ii) Can be tweaked while preserving BBB security? (iii) If the underlying random permutations can be computed efficiently in both forward and inverse directions, can we construct beyond-birthday secure PRFs by using only one permutation in both multi-key and tweakable cases?
Fortunately, we can give positive answers to these questions. First, we prove that is beyond-birthday secure in the multi-key setting. Informally, it means that for any distinguisher who distinguishes m independent n-to-n-bit keyed functions from m independent ideal random functions, its advantage does not depend on m. However, in this case the distinguisher still needs to make at least queries to achieve a noticeable advantage.
Second, we tweak the construction , inspired by the work []. A tweakable PRF, , means that one can associate a tweak space to the key space . For any key k randomly sampled from , one can choose different tweaks to compute even on the same input x.
Following the idea in [], we solve the third question, and construct beyond-birthday secure PRFs in the multi-key setting from one bidirectionally efficient random permutation. Then this new construction from a single permutation can also be tweaked while preserving BBB security.
1.1. Our Contributions
In this paper, we enhance the security of [] by showing that
is beyond-birthday secure in the multi-key setting, where ⊕ denotes the bitwise XOR operator, and are two independent random permutations, x is an n-bit input, and and are two n-bit uniformly random strings. Furthermore, we can tweak the construction , while preserving BBB security, as
where and are uniformly and independently sampled from the regular and almost-XOR universal (AXU) keyed hash family, t is a tweak, and x is an n-bit input.
Chen et al. [] first constructed beyond-birthday secure PRFs from random permutations. Later, Chakraborti et al. [] suggested and designed minimally structured beyond-birthday secure RPFs (i.e., by using only one random permutation). Following this line of study, we design a parallelizable beyond-birthday secure PRF in the multi-key setting from one bidirectionally efficient random permutation P as
where , , and x are the same as those in Equation (1). We tweak this new construction as
where , , x, and t are the same as those in Equation (2).
Moreover, from our two constructions of tweakble PRFs, we can give two nonce based MACs for variable length messages. In particular, when one replaces the input x (resp. the tweak t) in Equations (2) and (4) by an n-bit nonce N (resp. a message M), one can obtain two parallelizable beyond-birthday secure nonce based MACs as
and
1.2. Related Works
Based on two random permutations and , Cogliati et al. [] constructed a beyond-birthday secure tweakable Even-Mansour (TEM) as
where and are uniformly and independently sampled from the uniform and AXU keyed hash family, t is a tweak, and x is an n-bit input. Later, Dutta [] gave a beyond-birthday secure TEM from one permutation as
where P is a random permutation, and , , t, and x are the same as those in (7). Compared with Equations (7) and (8), our constructions in Equations (2) and (4) are parallelizable.
Chakraborti et al. [] constructed beyond-birthday secure PRFs from random permutations with minimal structure (i.e., from one random permutation P) as
where K is an n-bit key, x is an n-bit input, and 2 is a primitive element in the finite field so that denotes the multiplication of 2 and K over . Recently, Dutta et al. [] proved that the construction
is also a beyond-birthday secure PRF, where and are two n-bit uniformly random strings. However, all these two constructions were proved beyond-birthday secure only in the single-key setting. Compared with them, Equation (3) is parallelizable and can be proved beyond-birthday secure in the multi-key setting.
Besides, Chakraborti et al. [] also gave a nonce based MAC for variable length messages as
where K is an n-bit key, N is an n-bit nonce, M is a variable length message, and is uniformly sampled from the keyed hash family with three properties: regular, AXU, and 3-way regular.
1.3. Technical Overview
The basic technique to prove the BBB security of our constructions is the H-Coefficient technique [,]. As an example, we intuitively introduce the core idea of the security proof for the construction in Equation (4). Let be a random function from to , where is the tweakable space. Denote as in Equation (4). Given a deterministic distinguisher D who has access query to the primitive oracle P and to the construction oracle or , the goal of D is to distinguish which construction oracle it interacts with. Set as all p query-response tuples for the primitive oracle, and as all q query-response tuples for the construction oracle. Then, and along with and are called a transcript, denoted by . When D interacts with , the transcript is said in the ideal world; otherwise, is said in the real world.
In general, all possible transcripts are divided into bad transcripts and good transcripts. The key to use the H-Coefficient technique is to define bad transcripts in the ideal world with a low proportion. Furthermore, one also needs to show that the probability of any good transcript in the ideal world is close to its probability in the real world. After observing the transcript, the distinguisher will use this information to test whether it is compatible with . Based on this fact, one can briefly interpret how to define bad transcripts by the following example. Assume that there exist and such that and (this event is denoted by ). Then in the real world, one must have . However, in the ideal world, the probability that this equation holds is at most . In this case, the distinguisher has a significant advantage. If and are independently chosen from the uniform keyed hash family, then one has
By union bound, the probability of in the ideal world can be upper bounded by . This advantage is secure roughly up to adversarial queries. We illustrate some other bad cases for transcript in Figure 1, where (1) in Figure 1 is for the above example.
Figure 1.
Graphical representation of the motivation to define bad cases for the transcript in the ideal world, which corresponds to the bad conditions from (C-1) to (C-12) in Section 4. In this graph, the same color in different lines means that there exists a collision between these places.
For any good transcript, to prove that its probability in the real world is almost close to the one in the ideal world, it needs to show that the number of choices for unfixed maps of P is large enough. Let , , , and . Then the good transcript ensures that (resp. ) and all items in (resp. ) are distinct. The next goal is to choose distinct values for (resp. ) such that , , and all items in are distinct (resp. , , and all items in are distinct). However, this strategy is not enough to achieve the BBB security. To deal with this problem, we adopt the main idea in [,] to count more possible choices for unfixed maps of P, and this idea allows that . Informally, it means that there exist some pairs such that or . Take the first case for example, one has
To ensure that the maps in (9) are valid, can not be equal to previous fixed inputs of P, and can not be equal to previous fixed outputs of P. Since is a random function from to , then is uniformly and independently distributed for each distinct query in the ideal world. Due to this property, one can define the good transcripts to ensure that the number of rational maps in (9) is large enough. At the same time, it guarantees that the proportion of the corresponding bad transcripts in the ideal world can also achieve a beyond birthday bound. For more details, please refer to Section 4.
1.4. Organization
The rest of this paper is organized as follows. In Section 2, we introduce some necessary notations and basic tools. In Section 3, we prove the multi-key security of , further tweak the construction , and finally construct parallelizable nonce based MACs from two permutations. The constructions of beyond-birthday secure PRFs from one permutation in both multi-key and tweakable settings are given in Section 4, and we also design parallelizable nonce based MACs from one permutation in this section. Finally, Section 5 concludes this paper.
2. Preliminaries
2.1. Notations
For any , we simplify the set as , and denote the set of all n-bit strings by . For any finite set , means that s is sampled uniformly from . Besides, denotes the size of S. For any sets and , includes all functions from to , and we simply write for . Furthermore, denotes the set of all permutations on . For any two integers q and N such that , define . In particular, .
is said a well-defined n-bit permutation-compatible set if (resp. ) are all distinct. Given a well-defined permutation-compatible set , we say that the permutation extends , denoted by , if for all . For another well-defined n-bit permutation-compatible set , and are called disjoint if and for any and . Given the disjoint n-bit permutation-compatible set and , for any random permutation satisfying , the probability of is , which is denoted by
For any function , given the set , means that for any .
Given two sets U and , we say that U is disjoint with if . Let be a collection of finite sets. Then is called a disjoint collection if for any , is disjoint with . In this case, the size of is defined as . Two disjoint collections and are called inner disjoint if for any . Let be a multi-set, and let denote the multiplicity of x in . When is called a set, it means that all the repeated items in it are viewed as a unique item. Throughout this paper, when we discuss the size of , which is denoted by , the items in are counted without considering the multiplicity.
Definition 1
(Universal Hash Functions). Let n be a positive integer. Assume that and are two finite sets. Let be a keyed hash family from to , where is the hash key space. is called -regular if for any and any , it holds that
is called -almost XOR-universal (-AXU) if for any distinct and any , it holds that
is said XOR-universal (resp. uniform) if it is -AXU (resp. -regular).
Next, we briefly describe an example of -regular and -AXU keyed hash family [,] for some constant . Let M be any binary string with , and set . Then we pad M as , where , denotes the all zero s bits, and for each . For any , the keyed hash is defined as:
where and () are viewed as the elements in , and · denotes the multiplication in .
Remark 1.
The keyed hash family is said to be ϵ-3-way regular, if for any and any three distinct inputs t, , and , it holds that
2.2. The H-Coefficient Technique
One important tool used in our proofs is the H-Coefficient technique [], which can be used to upper bound the statistical distance between the query-answers from two interactive systems. For convenience, we focus on the modernization version of Chen and Steinberger [].
Let be r independent random permutations, and be the key space. In this paper, we only consider the case and . The randomly sampled -bit key can be parsed as , where and are two independent n-bit uniformly random strings. Then based on r public permutations , denotes the keyed function indexed by . Besides, let be an ideal random function. Then for any deterministic distinguisher who has query access to the oracle in the real world, or the oracle in the ideal world, the advantage of to distinguish which oracle it has access to is defined by
As shown in Figure 2, in the multi-key setting, the goal of distinguisher is to distinguish m keyed functions from m independent ideal random functions , where are m independent keys. In this case, let be the oracle in the ideal world, and be the oracle in the real world. The advantage of the distinguisher to distinguish these two oracles can be defined as the same in (11), but here we use to identify the multi-key case.
Figure 2.
The illustration of the RP-based keyed function in the multi-key setting, where the distinguisher D interacts with the real oracle at left, and with the ideal oracle at right.
Let be an -regular and -AXU keyed hash family from to . Then we use two independent keyed hash functions to tweak the keyed function as such that . In addition, the ideal tweakable random function can be denoted as , i.e., . In this case, let be the oracle in the real world, and be the oracle in the ideal world. For any distinguisher , its advantage can be defined as the same in (11), but here we use to identify the tweakable case.
The security proofs in both multi-key and tweakable settings are similar. Therefore, we prove these two cases in a unified approach. For two independently and randomly sampled functions and from , is said a good -key-derivation pair if it satisfies two properties in the following:
- (i)
- - For any and any , it holds that
- (ii)
- - For any distinct and any , it holds that
The above two properties are enough for the security proofs in both tweakable and multi-key settings. In the tweakable setting, is a good -key-derivation pair, where . In the multi-key setting, set , and uniformly and randomly sample two independent random functions . Then is a good -key-derivation pair. To show the security of the constructions in both tweakable and multi-key settings, we only need to prove the BBB security of the following “unified” function
where is a good -key-derivation pair and () are r independent random permutations. In this case, let be the oracle in the real world, and be the oracle in the ideal world, where . When the distinguisher interactes with or , any query-responses along with the good -key-derivation pair are called a transcript, denoted by . In addition, (resp. , for ) records query-responses when the distinguisher D interacts with the construction oracle (resp. the primitive oracle for ). Furthermore, (resp. ) denotes the probability distribution of the interacting transcripts between and (resp. ). A transcript is said attainable if . Finally, the advantage of the distinguisher , to distinguish which oracle it has access to, can be defined as the same in (11), but here we use to identify this unified description.
Let be a partition for the set consisting of all attainable transcripts, where (resp. ) contains all “good” (resp. “bad”) transcripts. Then the main result of the H-Coefficient technique can be described as the following lemma.
Lemma 1
(H-Coefficient Technique [,]). Let D be a deterministic distinguisher, and (resp. ) be the probability distribution of transcripts in the real world (resp. in the ideal world). Let and be defined above. Assume that there exists such that for any , it holds that
Then, .
2.3. Useful Tools
Assume that there are “rational” items in an N-size set S. When one samples items from S without replacement, denotes the random variable which counts the number of “rational” items among these items. Then we say that follows the hypergeometric distribution with parameters N, , and , denoted by . For , one has
In addition, the expectation value of is , i.e., .
The following lemma is useful in our proofs.
Lemma 2.
Let A, B, C, and N be positive integers satisfying and . Then we have
Proof.
where holds since and . □
3. Multi-Key and Tweakable Secure PRFs from Two Random Permutations
In this section, we prove that the construction from two random permutations in [], namely
is beyond-birthday secure in the multi-key setting, where and .
Let be an -regular and -AXU keyed hash family from to . Then we can tweak as
where , , and .
To show the security of in both multi-key and tweakable settings above, we only need to prove the BBB security of the following “unified” function
where , is a good -key-derivation pair, , and .
Theorem 1.
Let , and be a good -key-derivation pair. Consider the function defined in (14) based on two random permutations . For any deterministic distinguisher making at most queries to , queries to , and q queries to construction oracle or Φ such that , we have
In the multi-key setting, one sets corresponding to m independent random keys, and randomly samples two independent random functions . Then we can easily conclude that is a good -key-derivation pair. By this fact, one can obtain the following corollary.
Corollary 1.
Let . Consider the keyed function defined in (12) based on two random permutations . For any deterministic distinguisher making at most queries to , queries to , and totally q queries to (resp. m independent ideal random functions ) such that , we have
Corollary 1 shows that the construction in (12) is secure roughly up to adversarial queries in the multi-key setting.
Similarly, given an -regular and -AXU keyed hash family from to , one can obtain a good -key-derivation pair for , and finally conclude the following corollary.
Corollary 2.
Let , and be an -regular and -AXU keyed hash family from to . Consider the tweakable function defined in (13) from two random permutations . For any deterministic distinguisher making at most queries to , queries to , and q queries to or Φ such that , we have
Assume that is uniform (i.e., -regular) and XOR-universal (i.e., -AXU). Then Corollary 2 shows that in Equation (13) is secure roughly up to adversarial queries. This means that is a beyond-birthday secure tweakable PRF.
Finally, let denote a message space. Given an -regular and -AXU keyed hash family from to , we can construct a nonce based MAC (denoted by ), from two random permutations and , as
where , is message, and is a nonce. Due to assumption of , when we set , then is a good -key-derivation pair. Therefore, the following corollary holds.
Corollary 3.
Let , and be a message space. Let be an -regular and -AXU keyed hash family from to . Consider the nonce based MAC defined in (18) from two random permutations . For any deterministic distinguisher making at most queries to , queries to , and q evaluation queries, we have
Assume that for any message , one has for some integer . Then the keyed hash family from to can be instantiated by the defined in (10), which is -regular and -AXU. In this case, when one sets , then in (19) can be bounded as
If l is a constant, then is a beyond-birthday secure MAC.
Proof of Theorem 1.
For convenience, we follow some notations in [,] in this proof. Let be an attainable transcript, where , , and . In addition, we write these sets more clearly as:
We denote
and
For each , two associated sets can be defined as:
Now we define four parameters for transcript as
and can be also expressed as
where and .
An attainable transcript is said bad if any one of the following conditions is satisfied:
- (B-1): for , , and such that and .
- (B-2): for , , and such that and .
- (B-3): for , , and such that and .
- (B-4): for such that and .
- (B-5): for such that and .
- (B-6): for , and such that and .
- (B-7): for , and such that and .
- (B-8): for distinct tuples , such that and .
- (B-9): for and , such that , , and .
- (B-10): for and , such that , , and .
- (B-11): .
- (B-12): .
- (B-13): or .
Otherwise, we call a good transcript.
3.1. Analysis of Bad Transcripts
The proportion of all bad transcripts in the ideal world is upper bounded by the following lemma.
Lemma 3.
Let be the probability distribution of transcript in the ideal world, where , , , and is a good -key-derivation pair. Then we have
Proof.
Here we assume that there exists no repeated items in , , and w.l.o.g. Then for each distinct construction query , y is sampled uniformly and independently from in the ideal world. For each , the set of all transcripts satisfying (B-i) is denoted by . By union bound, one has
For each , the way to upper bound is similar to that in [,,]. Hence, we give the details in Appendix A. By combining these upper bounds together, the proof of Lemma 3 is finished. □
3.2. Analysis of Good Transcripts
In Lemma 4, we show that the probability of any good transcript in the real world is close to its probability in the ideal world.
Lemma 4.
Let be the probability distribution of transcripts in the ideal world, and be the probability distribution in the real world. Then for any good transcript , with parameters , , and q satisfying , one has
Proof.
Given a good transcript , we define the following probability
By a simple combinatorial argument, we have
The next goal is to lower bound . For convenience, define five subsets of as follows:
Note that and . The following proposition tells us that these sets form a partition of .
Proposition 1.
Let be a good transcript. Then the sets defined above are pairwise disjoint.
Proof.
By definition, we have , , and . Since does not satisfy (B-1), we have . Moreover, (resp. ) since does not satisfy (B-6) (resp. (B-7)). Finally, holds due to the fact . □
We use , , , , and to denote the events that , and , respectively. Then is equivalent to . Hence, it holds that
where
and
The way to compute and , and the way to lower bound are similar to those in [] so that we show the details in Appendix B. □
Finally, by Lemmas 1, 3, and 4, Theorem 1 can be proved. □
4. Multi-Key and Tweakable Secure PRFs from One Random Permutation
In this section, we first use one bidirectionally efficient random permutation to construct beyond-birthday and multi-key secure PRFs with a parallelizable structure as
where is the key and is the input.
Let be an -regular and -AXU keyed hash family from to . Then we can tweak the construction in Equation (22) as
where , , and .
As mentioned before, one can simultaneously show that the above two constructions are beyond-birthday secure in the multi-key and the tweakable settings by proving the BBB security of the “unified”function,
where is a good -key-derivation pair, , , and .
Theorem 2.
Assume that and are two positive integers. Let be a good -key-derivation pair, and be a random permutation. Consider the function defined in Equation (24). For any deterministic distinguisher making at most p queries to P and q queries to the construction oracle or Φ such that , one has
Same to Corollary 1, the following corollary holds.
Corollary 4.
Assume and are two positive integers. Let be an n-bit random permutation. Consider the keyed function defined in (22). For any deterministic distinguisher making at most p queries to P and at most totally q queries to (resp. m independent ideal random functions ) satisfying , we have
Similarly, given an -regular and -AXU keyed hash family from to , the following corollary holds.
Corollary 5.
Assume and . Let be an -regular and -AXU keyed hash family from to , and be an n-bit random permutation. Consider the tweakable function defined in (23). For any deterministic distinguisher making at most p queries to P and q queries to or Φ such that , we have
Denote as a message space. Let be an -regular and -AXU keyed hash family from to . Then we can construct a nonce based MAC denoted by , from one random permutation as
where , is message, and is a nonce. In this case, is a good -key-derivation pair, and we can obtain the following corollary.
Corollary 6.
Assume and . Let be an -regular and -AXU keyed hash family from to . Consider the nonce based MAC defined in (28) based on a random permutation and . For any deterministic distinguisher making at most p queries to P and q evaluation queries, we have
Let denote a message space, where for some , holds for each message . Then, the keyed hash family from to can be instantiated by the defined in (10), which is -regular and -AXU. In this setting, when l is set to a constant, then is a beyond-birthday secure MAC.
Proof of Theorem 2.
In this proof, we follow some notations in [,] for convenience. Let be an attainable transcript with and . We write these sets more clearly as follows:
We also denote
as domain and range of respectively. For each , two associated sets can be defined as:
Now we define four parameters for transcript as
where and can be also expressed as
where and .
An attainable transcript is said bad if any one of the following conditions is satisfied:
- (C-1): and for , , and such that and .
- (C-2): and for , , and such that and .
- (C-3): and for , , and such that and .
- (C-4): and for and such that and .
- (C-5): and for and such that and .
- (C-6): and for and such that and .
- (C-7): and for and such that and .
- (C-8): for such that and .
- (C-9): for such that and .
- (C-10): and for pairwise distinct and such that and .
- (C-11): and for and , such that , and .
- (C-12): and for and , such that , and .
- (C-13): .
- (C-14): .
- (C-15): or .
- (C-16): and for pairwise distinct and such that and .
- (C-17): For sets , , and derived from the transcript, or .
Otherwise, is said a good transcript.
4.1. Analysis of Bad Transcripts
Let be the set of all transcripts satisfying (C-i) for . The proportion of all bad transcripts in the ideal world can be upper bounded in the following lemma.
Lemma 5.
Let be the probability distribution of transcript in the ideal world, where , , and is a good -key-derivation pair. Then we have
Proof.
Let be any attainable transcript in the ideal world, where includes p permutation pairs from the interaction between distinguisher D and P. For each distinct construction query , y is sampled uniformly and independently from . Without loss of generality, we assume that there exists no repeated items in and .
The probabilities of in can be upper bounded as
For , one can obtain the following upper bound
and more details can be found in Appendix C.
For , we need to study (C-16) and (C-17), respectively.
(-): For any three distinct construction queries , , and , and are independently and uniformly sampled from . Hence, we have
Since the number of all possible tuples for is at most , by union bound, one has
(-): First, we have (which means ). Hence, by the definition of , it holds that . Similarly, we also have (which means ) and . By combing these facts and the definitions of , , and , the random value y for each in the ideal world is independent of any elements in and . Therefore, for each pair , one has
Then the expectation value of random variable can be bounded as
By Markov’s inequality, we have
Similarly, it holds that
Therefore, one has
Finally, by combining the upper bounds on and together, by (30), the proof of Lemma 5 is finished. □
4.2. Analysis of Good Transcripts
In this part, we prove that for any good transcript , the probability to sample it in the real world is close to that in the ideal world, and this result can be formally stated in the following lemma.
Lemma 6.
Assume that and . Let be the probability distribution of transcripts in the ideal world, and be in the real world. Then for any good transcript with parameters p and q satisfying , one has
where .
Proof.
Given a good transcript , we define the following probability
By a simple combinatorial argument, it holds that
We first introduce some subsets of as follows:
Note that , , and has been defined in (C-17). In fact, these sets form a partition of .
Proposition 2.
Let be a good transcript. Then defined above are pairwise disjoint.
Proof.
By the definition of these five subsets, it holds that , , and . Since does not satisfy (C-1), one has . Besides, (resp. ) holds since does not satisfy (C-6) (resp. (C-7)). Finally, since . □
We use , , , , and to denote the events , and , respectively. Note that is equivalent to . Therefore, it holds that
where
and
The next goal is to lower bound and .
. Conditioned on , P is fixed on exactly p input-output pairs from U to V. For each , there exists a unique satisfying . Hence, . Then we define two sets:
All values in (resp. ) are distinct since does not satisfy (C-11) (resp. (C-6)). Moreover, since and , one has and respectively.
For each , there exists a unique satisfying . In this case, . Then we can define two sets:
All elements in (resp. ) are distinct since does not satisfy (C-7) (resp. (C-12)). Due to the fact and , one has and , respectively. Moreover, (resp. ) since (resp. ). Besides, it holds that and . Therefore, one can obtain that
Now, we can define two disjoint collections and . In this case, P is fixed on exactly input-output pairs from to .
. When conditioned on , we next lower bound the number of all possible “new” and distinct input-output pairs of P such that the event happens. First, one can define some multi-sets associated to and as follows:
Let , , , and . For convenience, we rewrite these sets as:
Let , , , and . These sets can be written more clearly as:
Recall that and . Then, we get
Similarly, it also holds that and . Since , there exists no repeated items in and . Hence, one can conclude that and . Now we define two multi-sets associated to as
By the definition of , there exists no repeated items in and . Based on these two sets, one can define two corresponding sets as:
Set and as two set collections. Then we can conclude the following proposition.
Proposition 3.
With notations as above, one has
- (i)
- All sets in (resp. ) are disjoint, i.e., , , and (resp. , , and ).
- (ii)
- is inner disjoint with , and is inner disjoint with .
Proof.
We first prove (i). From the fact , we have . By the definition of and , one can conclude that . holds due to the fact , and the disjoint property of and . We can conclude that , , and in a similar way.
Next we prove (ii) by enumerating all possible cases. For , the definition of means that ; comes from the fact ; holds due to the disjoint property between and , and the fact . For , comes from the fact , and the definition of ; comes from the fact ; By the disjoint property between and , and the fact , we have . For , the definition of means ; comes from the fact that ; By the disjoint property between and , and the fact , we has .
For , comes from the fact , and the definition of ; can be derived from the disjoint property between and , and the fact ; The fact means . For , holds from definition of ; comes from the definition of , and the fact ; The fact means . For , comes from definition of ; holds due to the disjoint property between and , and the fact ; Finally the fact means . □
Now we define two disjoint union sets (which equals to in (C-17)), and (which equals to in (C-17)).
Let (actually, ) and . Then it holds that if . Next we try to sample “new” values for and by allowing that there exist many construction queries such that or holds. In the first case, we can obtain three maps like
In the second case, we have
If and , or and , then the above permutation maps are compatible with and . Intuitively, when we consider the above “collision” maps, there would be as many permutations chosen to be compatible with and as possible so that our construction can achieve BBB security.
Conditioned on , we next describe all possible permutations satisfying , and finally compute and lower bound .
For each , we define the following set
where for each , one has (resp. ) for some query and (resp. ) for another query .
Definition 2.
We say a “good” set if the following four conditions are all satisfied
- (1)
- ,
- (2)
- ,
- (3)
- , for any ,
- (4)
- , for any .
The next lemma shows that for each , the number of all possible “good” sets derived from is close to .
Lemma 7.
Assume that and . Let α be an integer with . Let be the number of all “good” sets derived from . Then we have
where .
Proof.
We count all possible pairs in a “good” set step by step as follows. First, we decide all possible pairs for . There are possible pairs to be chosen for . Since , there are at most pairs not satisfying the first two conditions in Definition 2. Then we can choose at least possible pairs for .
After choosing , we decide all possible in the following way. We first choose from the remaining possible pairs, and then choose the corresponding pair outside of , , and to satisfy all four conditions in Definition 2. To satisfy the last two conditions and in Definition 2, and should chosen such that
In this case, from the definition of and the fact , it excludes at most 3 possibilities to be chosen for . Then there are at least possibilities to be chosen for , when we only consider the last two conditions in Definition 2. Finally, from the fact , there are at most pairs to be removed for all possibilities if we want them to satisfy the first two conditions and in Definition 2. Overall, there are at least possible pairs to be chosen for .
After choosing pairs , …, , there are at least possible pairs to be chosen for by repeating the above step.
When we finish the choice of all possible cases for , satisfying all four conditions in Definition 2, one can conclude that
where the term appears because the set S is unordered.
Furthermore, can be lower bounded as follows
where (i) follows as , (ii) follows as , (iii) follows as , and (iv) follows as if . □
For a fixed with and a corresponding “good” set
the following assignment (34) for P is well-defined by the definition of S:
Furthermore, based on the “good” set S, we define two subsets of and as
Besides, we can also denote two additional sets as
where (resp. ) and all items in (resp. ) are distinct. After the assignment (34) for P, P is fixed on input-ouput pairs from to . In addition, we can define the corresponding co-subset of and as and , respectively.
Until now, the random permutation P is fixed on p input-output pairs from U to V, input-output pairs from to , input-output pairs from to , and input-output pairs from to . Based on these facts, the next work is to choose all other possible compatible items for , , , , and to extend the fixed input-output pairs of P.
Note that once the items in are fixed, the corresponding items in are uniquely determined since these two sets are both derived from . Similarly, the items in (resp. ) uniquely determine the items in (resp. ). Then we sample all possible items for these sets through three steps.
.
Let and . The size of is , and the size of is . Recall that and . Let be the number of distinct tuples in such that the following two conditions are satisfied
- (i)
- , for each where , .
- (ii)
- with , for each , should be satisfied for each .
Now we count the number of all possible distinct tuples satisfying these two conditions. First, one has . The first condition can remove at most items for each k, and the second condition can exclude at most values for each choice of . By the choice of above, we obtain that
Let and . The first condition ensures that is disjoint with . Items in are distinct due to the second condition and the fact . This fact tells us that for each and , it holds that but , which means that . Moreover, items in are distinct, and is disjoint with by the choice of . Let , and . The size of is , and the size of is .
.
Recall that . Let be the number of all distinct tuples in satisfying the following two conditions:
- (i)
- , for each , .
- (ii)
- with , for each , should be satisfied for each .
Now we count the number of all possible distinct tuples satisfying these two conditions. Similarly, one has . The first condition can remove at most values for each k, and the second condition can exclude at most items for each choice of . By the choice of , we obtain that
Let , and . It holds that items in are distinct. Furthermore, is disjoint with by the choice of . Let , and . The size of is , and the size of is .
.
Let (). Let m be the number of all distinct tweaks appearing in , and then we use to denote these m distinct tweaks. We denote and . In this case, it holds that . For convenience to count, we denote and rewrite the items in indexed by the m distinct tweaks as
For and , denote
For convenience, and can be written as and , respectively. Let be all possible different tuples in such that the following two conditions are satisfied.
- (i)
- For each and , .
- (ii)
- For each and , is distinct from the values for and . Furthermore, should be distinct from the values for with .
Except these two conditions, each must be different from each other. By a simple computation, one has , where and . So . Now we bound the number of all possible distinct tuples satisfying these two conditions. The first condition excludes at most values, and the second condition excludes at most values for each choice of . Furthermore, should not be same as any one of previous items. By combining these facts, one can conclude that
Overall, by combining (33), (35), (36), and (37), one has
By combining (32) and (38), we have
Recall that
Lower bounds on , , and are given in Appendix D, and the results are showed as follows:
where , , and .
Putting (42), (43), and (44) into (41), we obtain
The last term in (45) can be bounded as
where follows as Markov’s inequality and follows as which comes from the assumption and the fact . Let . Then we can write (45) as
Combing all these facts together, the proof of Lemma 6 is finished. □
Finally, by Lemmas 1, 5 and 6, Theorem 2 follows. □
5. Conclusions
In this paper, we first prove the BBB security of the construction in the multi-key setting, and further tweak this construction. When the bidirectionally efficient public random permutations are considered, we build the parallelizable beyond-birthday secure PRFs from one permutation in the multi-key setting, and also tweak this new construction while preserving BBB security. By a slight modification of two tweakable PRFs, we obtain two parallelizable nonce based MACs for variable length messages. In fact, the constructions mentioned above come from sum of two Even-Mansours. It is natural to generalize to sum of s Even-Mansours, namely
where are s independent random permutations, and are s n-bit uniformly random strings. Obliviously, this generalization is at least as secure as even in the multi-key setting. However, the detailed analysis of its security is not easy to see, and we leave it as a future work.
Author Contributions
Writing—original draft, J.N.; Writing—review & editing, P.Z. and H.H. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported in part by the National Natural Science Foundation of China (Nos. 61632013 and 61972370), and by Fundamental Research Funds for Central Universities in China (No. WK3480000007).
Informed Consent Statement
Informed consent was obtained from all authors included in the study.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A. Upper Bound on in Lemma 3
For each , we upper bound as follows.
(-), (-), and (-): First, we consider (B-1). For any , , and , by the -regular property of , one has
Since the number of all possible tuples for is , by union bound, it holds that
Similarly, we can bound the probabilities of (B-2) and (B-3) as
(-) and (-): For any two distinct queries , by the -AXU property of pair , we have
Since there are possible unordered pairs for , by union bound, one can obtain that
(-) and (-): For any two distinct construction queries and any , by the -regular and -AXU properties of , we have
Then, summing over all and , one has
(-): For any with and , by the -AXU property of , one concludes that
Note that the above inequality also holds for the case (resp. ) since we have (resp. ) i.e., (resp. ). It is easy to count that the number of all possible is at most , which means that
(-), (-), (-), and (-): We deal with bad conditions (B-9) and (B-11) together by using the fact that
We first consider how to upper bound . For the random variable (the randomness from the choice of ), its expectation value can be computed as
due to the -regular property of . By Markov’s inequality, one has
Under the condition , there are at most pairs , such that and where and . In this case, the corresponding and are two independently uniform random variables over so that we have
By summing over all the possible pairs, one can obtain that
Finally, it holds that
Similarly, we obtain
(-): To bound , we first define the random variable . By definition of , one has
Hence, . We can compute the expectation value of as
from the -AXU property of . By Markov’s inequality, one has
Finally, we obtain
Appendix B. More Details in Proof of Lemma 4
. Conditioned on and , (resp. ) is fixed on exactly (resp. ) input-output pairs. For each , there exists a unique satisfying so that . Then we can define two corresponding multi-sets as:
Note that all values in (resp. ) are distinct since otherwise would satisfy (B-6) (resp. (B-9)). Then it holds that . Moreover, since and , one conclude that and , respectively. Then we get
Similarly, for each , there exists a unique satisfying , which means . Then two corresponding multi-sets can be defined as:
All values in (resp. ) are distinct since otherwise would satisfy (B-7) (resp. (B-10)). Then one has . Moreover, since and , it holds that and , respectively. Hence,
By combing (A1) and (A2), one can conclude that
Now it holds that and . Then we define four disjoint collections , , , and . Notice that when conditioned on , is fixed on exactly input-output pairs and is fixed on exactly input-output pairs.
. Conditioned on we next lower bound the number of all possible “new” and distinct input-output pairs of and such that the event happens. We first define four multi-sets derived from and as:
The size of four sets above can be denoted as , , , and . We also denote four additional sets as , , , and , which can be wrote more clearly as:
For convenience, we rewrite and as:
Recall that and . Then and can be bounded as:
Similarly, we obtain and . From the fact , one has that any items in the (resp.) are distinct so that (resp. ) holds. Finally, we define two multi-sets derived from as
Due to the definition of , it holds that any items in (resp. ) are distinct. We can also denote two additional sets as
Let (besides, ). Let m be the number of all distinct tweaks appearing in , and then we use to denote these m distinct tweaks. Furthermore, write as a set consisting of all the query-response tuples indexed by the tweak in and denote ( might be zero for some i). Then it holds that and respectively . For convenience to count, we rearrange the items in as
For and , we denote
For convenience to describe, we rewrite the sets and as
Let and . Then the following proposition holds.
Proposition A1.
With notations as above, we have
- (i)
- All sets in (resp. ) are disjoint, i.e., , , and (resp. , , and ).
- (ii)
- is inner disjoint with and is inner disjoint with .
Proof.
We first prove (i). From the fact , one can conclude that . By definition of and , holds. By combining the fact and the disjoint property of and , one has . We can conclude that , , and in a similar way.
Next we prove (ii) by enumerating all possible cases. For , the definition of means that ; comes from the fact . For , holds due to the fact ; can be obtained from the fact and the disjoint property between and . For , the definition of means ; holds for the reason that and is disjoint with .
For , the definition of means that ; comes from the fact . For , one has since ; holds due to the fact and the disjoint property between and . For , one has by the definition of ; holds for the reason that and is disjoint with . □
Until now P is fixed on input-output pairs from to , input-output pairs from from , input-output pairs from to , and input-output pairs from to . Based on these facts, the next work is to choose other possible compatible items for , , , , , and to extend the fixed input-output pairs of permutations and , respectively.
Note that once the items in are fixed, then the corresponding items in are uniquely determined since these two sets are both derived from . Similarly, the choices for items in (resp. ) uniquely determine the items in (resp. ). Then we sample all possible items for these sets through three steps.
.
Recall that and . Let be the number of -wise tuples of distinct values in satisfying the following two conditions:
- (i)
- For each and each , .
- (ii)
- For each and , is distinct from the values , for and .
Now we count the number of all possible distinct tuples in satisfying the above two conditions. First, we have . The first condition can remove at most values, and the final condition can exclude at most values for each choice of . By combining above facts, one gets that
In Condition (ii), for each and , it holds that (which is equivalent to ) from the fact . After choosing any tuple of distinct values such that Conditions (i) and (ii) hold, we define two corresponding sets as follows:
From the above discussion, we know that all values in are distinct, and all values in are also distinct. By the choice of , it holds that and . After this step, is fixed on input-output pairs from to , and is fixed on input-output pairs from to .
.
We next deal with . Recall that and . Let be the number of -wise tuples of distinct values in such that the following two conditions hold:
- (i)
- For each and each , .
- (ii)
- For each and , is distinct from the values , for and .
Now we count the number of all possible distinct tuples in satisfying above two conditions. It is easy to see that . The first condition can remove at most values, and the final condition can exclude at most values for each choice of . Then we can bound as
In Condition (ii), for each i and , it holds that (which is equivalent to ) since otherwise would satisfy Condition (B-5). Similarly, we define two sets as:
By the discussion above, all values in are distinct and all values in are also distinct. Then and hold from the choice of . After this step, is fixed on input-output pairs from to , and is fixed on input-output pairs from to .
.
It remains to sample all possible compatible values in and . First, we denote and as
Recall that and . Let be the number of -wise tuples of distinct values in such that the following two conditions hold:
- (i)
- For each and , .
- (ii)
- For each and , is distinct from the values for and . Furthermore, should also be distinct from the values with .
Except these two conditions, each must be chosen distinctly from each other. First, one has . Then we count the number of all possible distinct tuples satisfying above two conditions. The first condition can exclude at most values, and the second condition can exclude at most values for each choice of . Furthermore, should not be same to previous items. Based on these facts, one can obtain that
Until now, we have chosen possible values for , , and satisfying all above conditions. By this way, when conditioned on , the event happens means that (resp. ) is fixed on exactly (resp. ) “new” input-output pairs from (resp. ) to (resp. ). Finally, we conclude that
From (A3) and (A7), one has
Combining (21) and (A8), we get
where can be obtained from the fact .
First, can be bounded as follows:
where the last equality holds from the fact , , and .
Next, we can bound as
where the last equality holds from the fact , , , and .
Finally, can be bounded in the following way:
where holds by Lemma 2 when one sets , , and such that and , follows as and , and follows as .
We finally lower bound , from (A9), (A10), (A11), and (A12), as
Appendix C. Upper Bound on
In this part, we upper bound each term for one by one.
(-), (-), and (-): For any and , , by the -regular property of , one has
Since the number of all possible tuples for is at most , by union bound, it holds that
Similarly, we can bound the probabilities of (C-2) and (C-3) as
(-) and (-): For any fixed construction queries , and , by the same reason as above, we have
Since there are at most possible unordered pairs for , , by union bound, one obtains that
(-) and (-): For any fixed distinct construction queries and , from the -regular and -AXU properties of , one has
Since there are at most possible unordered pairs for , , by union bound, it holds that
(-) and (-): For any two distinct construction queries , one can conclude
from the -AXU property of . In particular, when , the above probability is in fact zero since in this case we have but . Then by summing over all possible unordered pairs , one has
(-): For any , and with and , one can conclude, from the -AXU property of , that
Note that the number of all possible tuples is at most so that one has
(-), (-), (-), and (-): We deal with bad conditions (C-11) and (C-13) together by using the fact that
We first consider how to upper bound . Recall that . Then the expectation value of can be computed as
due to the -regular property of . By Markov’s inequality, one has
Under the condition , there are at most pairs , such that and where and . In this case, since the random variables and are independently and uniformly distributed over , one can conclude that
By summing over all these possible pairs, we have
and so that it holds that
Similarly, we can obtain that
(-): To upper bound , we first define the random variable . By definition of , it holds that
Thus, . Then the expectation value of can be bounded as
from the -AXU property of . By Markov’s inequality, we have
Similarly, one has
Finally, by combining the above two facts, it holds that
Appendix D. More Details in Proof of Lemma 6
First we have
where follows as so that , and follows as and .
Then, the item can be bounded as
where follows as so that and follows as and .
Finally, can be bounded in the following.
For , we have
where follows as for and and follows as .
We then bound the as
where follows as Lemma 2 when we set , and where it satisfies from the assumption and follows as .
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