Abstract
The emergence of fog-based Internet of Things (IoT) systems have played a significant role in enhancing the applicability of the IoT paradigm. In such systems, fog-nodes are proficient enough to retain, process and transmit the data coming from IoT devices. Nevertheless, as an extension of cloud computing, inheriting the security and privacy concerns of cloud computing is also inevitable in fog-based IoT systems. To deal with such challenges, a diverse range of security solutions are reported in the literature. However, most of them have several limitations (i.e., vulnerability to known security attacks and high computation overhead) that curtail their practical implementation applicability. Keeping these limitations in mind, this paper propose a privacy-preserving hash-based authenticated key agreement protocol using XOR and concatenation operations for fog-driven IoT systems. Using healthcare as a case study, the security of the novel protocol is evaluated by using informal and formal security analysis. In order to obtain the experimental results, the key cryptographic operations used at the user, fog node and cloud server-side are implemented on a mobile device, Arduino and cloud server, respectively. Findings from the performance evaluation results show that the proposed protocol has the least computation cost compared to several related competing protocols.
1. Introduction
In this modern era, the healthcare system has proved to be an influential industrial IoT (IIoT) application in which IoT devices are deployed either at enormous healthcare centers or small private clinics. Moreover, the increasing popularity of the several wearable smart IoT devices has significantly transformed the typical healthcare system from an originally hospital-centric to a patient-centric system. Such a patient-centric approach bridges the gap between practitioners and patients by offering reliable and seamless delivery of sensitive information. It allows citizens to contact practitioners remotely and be availed of medical advice and prescriptions at their doorstep. For instance, wearable smart IoT devices (i.e., heart rate monitors, Electrocardiograms (ECG) monitoring instruments, etc.) are proficient enough to sense, inspect and stream information to various healthcare professionals without any requirement of a physical visit to the hospital [1,2].
The healthcare system is considered a particular industry that requires minimized latency. For instance, a delay of only a minute can cause damage to the life of an individual who is suffering from a heart attack. Therefore, in this context, a remarkable fog-driven solution is considered. A fog-driven healthcare system based on IoT is illustrated in Figure 1. It is obvious from Figure 1 that the fog-driven healthcare system based on IoT comprises three layers including (i) the cloud layer of IoT healthcare, (ii) the fog layer of IoT healthcare and (iii) the device layer of IoT healthcare [3,4].
Figure 1.
IoT based fog-driven health monitoring system.
Conventionally, fog computing exhibits classified infrastructure which consists of the fog-nodes and cloud data centers. The sequential architecture of fog computing is depicted in Figure 2, where the sensors and connected IoT devices are utilized to manage unique tasks such as physical objects monitoring, the collection of data related to objects, and communication of data towards the fog-nodes. All the involved devices are further connected to the nearest fog-nodes via numerous communication protocols. The fog-nodes installed at suitable sites are responsible for storing, processing and transmitting the collected data of different objects. The fog-nodes command the data from both cloud and user. Further, they are responsible for refining the collected data and transmitting this accumulated data towards the cloud for interpretation or storage. However, the cloud data center comprises storage devices and high-performance servers. It offers networking, computing and storage facilities for large scale data. Furthermore, the core network and edge network ensure two-way communication for the smooth transmission of data among IoT devices, fog-nodes and the cloud data center.
Figure 2.
Hierarchical Infrastructure of the Fog Computing.
Contrary to the concept of cloud computing, which is purely considered a centralized system, the deployment of fog-nodes is performed without any suitable safety scope. Simply, fog-nodes and end devices have a high chance of being compromised. This situation could lead to privacy violation through leakage of users’ information, such as location, identity, medical records and health status, etc.
Table 1 gives an in-depth summary of contemporary existing key agreement protocols with respect to the techniques employed by each protocol, and their limitations against various attacks, in chronological order. Moreover, it is noticeable that no work in the existing literature supports user revocation to the best of our knowledge. In summary, the secure, robust and practical design of an authentication protocol for a fog-based environment is still a challenging job. Keeping this in mind, this paper introduces a three-party identity-based authenticated key agreement protocol for a fog-driven IoT healthcare framework by focusing on the security and privacy challenges faced during the communication.
Table 1.
Summary of existing studies.
In order to prevent impersonation and to establish a firm trust, each fog-node or user in the system must be authenticated and identified uniquely. Further, to ensure privacy and security, there is a vital need to encrypt the data that are transmitted via open medium and then stored in the cloud server or fog-nodes. Nonetheless, the pre-sharing of session keys between the end devices and the fog-nodes is not realistic due to the mobile and dynamic nature of various fog-nodes and end devices. Thus, AKA (authenticated key agreement) protocol provides the best solution for authenticating nodes or users and generating session keys [14]. The main contributions of this article are as follows:
- This paper presents a three-party identity-based authenticated key agreement scheme to secure the communication of data among the participants in an IoT based fog-driven healthcare system. The robust identity-based three-party AKA protocol is designed using hash, XOR and concatenation operations.
- The proposed protocol ensures mutual authentication among users, fog-nodes and cloud service providers for the establishment of a shared session key. This shared session key is used by all the participants.
- Our designed protocol guarantees that the identity of any user can not be exposed to any adversary except the cloud server.
The rest of our article is formulated according to the following. The complexity assumptions are illustrated in Section 2, while introduced protocol is presented in Section 3. Section 4 describes the security analysis of our presented scheme. Performance comparison of the proposed protocol and related competing protocols is illustrated in Section 5. Lastly, we have concluded our work in Section 6.
2. Complexity Assumptions
This section briefly describes the communal threat model. Moreover, the commonly used notations throughout the article are presented in Table 2.
Table 2.
Commonly used notations.
Threat Assumption Model
In order to discuss the capabilities of an adversary, an adversarial model is briefly described as mentioned in [15,16]. An adversary has the following capabilities:
- has full control over the public channel.
- can be a user or cloud service provider.
- is assumed to be honest but curious, whereas, is assumed as untrustworthy entity; because of that, it does not keep any verifier of .
- can intercept the messages that are exchanged over the public channel between , or .
- can modify and replay the intercepted messages to misuse them.
- The cloud service provider is a secure entity, so, cannot access it.
- can be an insider or outsider. An outsider can be a deceitful user and internal can be a cloud server, provider or user.
3. Proposed ITP-AKA Protocol
This section demonstrates our proposed identity-based three-party authenticated key agreement (ITP-AKA) protocol. Detailed descriptions of all these phases are shown in Section 3.1, Section 3.2 and Section 3.3, respectively. Login and authentication phase was shown in Figure 3.
Figure 3.
Login and authentication phase.
3.1. User Registration Phase
Initially, user imitates the process to get himself registered with the cloud service provider . For this purpose, performs the subsequent steps:
- 1:
- randomly chooses , identity and computes . sends to via a secure channel.
- 2:
- After receiving request, computes the following values: . Further, selects for the respective user and calculates and stores in its database corresponding to and using x. then transmits to using a private channel.
- 3:
- After receiving the message from , computes and stores in its tamper-proof device.
3.2. Fog-Node Registration Phase
In order to deploy the fog-node, it must initially be registered with , according to the subsequent steps:
- 1:
- sends its identity to the . On obtaining the identity, calculates and selects . stores in its database corresponding to using x, and transmits to the fog-node via a secure channel.
- 2:
- After taking the message from , stores in its tamper-proof device.
3.3. Login and Authentication Phase
This phase of proposed protocol finishes with the below-mentioned subsections taken by all the participants:
- Step#1 • Login:
- inputs its identity and computes , , . After that, verifies . If the condition is true, generates arbitrary numbers , and calculates , and forwards the login request message towards . Otherwise, the login request by is rejected. .
- Step#2 • Login:
- After receiving the authentication request from , generates an arbitrary numbers , and computes , , . forwards to the . .
- Step#3 • Authentication & Key Establishment:
- After receiving the authentication request message from , extracts user’s corresponding to from its database using x, and determines , . For authenticating the user, verifies . If the condition is false, the current session will be ended. Otherwise, extracts fog-node corresponding to from its database using x, and determines , . Now, for authenticating the fog-node, verifies . If this condition is wrong, the session will be aborted. Once the aforementioned conditions are true, then generates an arbitrary number and computes , , , . Furthermore, selects for the user and for the fog-node and computes , and updates the value of and for the user and the fog-node in its database and transmits the response message to the . .
- Step#4 • Key Establishment:
- On receiving the response message from , computes , . Furthermore, authenticates by verifying . If this condition is wrong, the current session will be ended. Otherwise, it will calculate and update the value of in the tamper-proof device of the fog-node and relays to . .
- Step#5 • Key Establishment:
- On receiving the response message from , computes . Moreover, authenticates by verifying . If this condition goes wrong, the current session will be ended. Otherwise, it will calculate and update the value of in the user tamper-proof device.
3.4. User Revocation Phase
Several circumstances in the fog computing system require the revocation mechanism to revoke a compromised ’s account from the system. Assume that once a ’s memory device is lost, revokes their account with using the same . The mentioned points are critical to execute the user revocation phase:
- 1:
- randomly chooses a new , identity and computes . sends to using a private channel.
- 2:
- After getting request, computes the following values . Afterwords, selects for the respective and calculates and stores in its database corresponding to using x. then transmits to via a private channel.
- 3:
- After getting the message from , computes and storesin its tamper-proof device.
4. Security Analysis
In this section, the security of the fog-based proposed protocol for an IoT healthcare environment is analyzed. The proposed protocol is evaluated formally and informally to determine its security strength. A detailed description of the security features is described in subsections.
4.1. Informal Security Analysis
This analysis shows that the proposed protocol is secure against different attacks. Furthermore, it shows that the proposed protocol achieves mutual authentication and ensures user anonymity.
4.1.1. Providing Mutual Authentication
In our proposed protocol, both participants and mutually authenticate each other to ensure their legitimacy. authenticates by checking whether holds or not. Any adversary cannot pass this check, as he cannot compute valid , because it involves the secret key x of . So, only authenticates the legitimate user . Likewise, authenticates while checking . The valid can pass this check because an adversary cannot know the value of the legitimate . Furthermore, an adversary cannot compute because he does not have any knowledge of server’s secret key. Hence, the proposed protocol provides mutual authentication among and .
4.1.2. Providing User Anonymity and Untraceability
User anonymity is considered an important security feature while designing authenticated key agreement protocols. The proposed protocol ensures the anonymity of user because instead of transmitting the identity of in plain text over a public channel, is forwarded, which is randomly selected by and not known to any adversary. Similarly, in every session, dynamic is transmitted over a public channel. Therefore, no adversary can determine the identity of user. Consequently, the devised protocol is resistant to traceability attacks.
4.1.3. Resisting Tamper-Proof Device Stolen Attacks
The holds in a tamper-proof device . It is quite possible that an adversary can steal , extract all the values stored in it and initiate a valid request message. In our proposed protocol, an adversary cannot compute a valid login request message even after stealing . If an adversary wants to calculate a valid login request message , he will have to compute a valid . The computation of is not possible for an adversary as he can neither compute the valid value of , nor can he retrieve the value of the random number . Further, the adversary does not know the identity of . So, the proposed protocol is secure against tamper-proof device stolen attacks.
4.1.4. Resisting User Masquerading Attacks
In the proposed protocol, legitimate user sends the login request message to , and relays the message towards . Suppose an adversary intercepts the login request message initiated by and tries to impersonate the legal user . In our proposed protocol, even after breaching the login request message , cannot compute a valid , as the computation of an requires the of which is unknown to . So, cannot masquerade as the legitimate user.
4.1.5. Resisting Fog-Node Masquerading Attacks
During the login and authentication phase of proposed protocol, fog-node transmits a message towards cloud service provider . As the message is transmitted over a public channel, can intercept this message and try to impersonate the . Therefore, in order to impersonate the fog-node successfully, needs to re-generate the message . It is obvious from Section 4.1.4 that cannot compute a valid , as it involves the of , which is unknown to . Likewise, the computation of is also not possible for , due to the involvement of . Therefore, it is quite impossible for to re-generate the message . So, the impersonation of is not possible in our proposed protocol.
4.1.6. Resisting Cloud Service Provider Masquerading Attacks
In the proposed protocol, transmits the challenge message towards through . Suppose an adversary intercepts the challenge message; even then, he cannot impersonate the valid . Although can compute all the other values of challenge message, he cannot compute a valid and . The computation of and needs the calculation of valid and which is only possible with secret key x of . As, x is not known to , so cannot impersonate valid . This is how our protocol resists a masquerading attack.
4.1.7. Stolen-Verifier Attacks
In the proposed protocol, maintains a database to store the identities of and , respectively. However, the real identities of and are encrypted using the private key x of . Since x is the private key of , which is only known to , cannot extract . Hence, our protocol resists stolen verifier attacks.
4.1.8. Resisting Known Session Key Attacks
During the computation of session key session specific random numbers , and are used. So, the computed session key of each session is not dependent on any other session. Therefore, even if the session key of one session is leaked, there will be no impact on the privacy of other session keys.
4.1.9. Perfect Forward and Backward Secrecy
It is clearly mentioned in Section 4.1.8 that even the leakage of one session key does not impact upon the privacy of upcoming/previous session keys. Therefore, the devised protocol offers perfect backward as well as forward secrecy.
4.2. Formal Security Analysis
In this subsection, the formal security analysis of the proposed protocol is discussed using random oracle model (ROM) and automated validation of internet security protocols and applications (AVISPA).
Random Oracle Model
In this subsection, the formal security analysis of the proposed protocol is discussed using the following theorem.
Theorem 1.
Let P denote the proposed protocol. If an adversary wins the AKA attack game, having advantage , then there must be a polynomial time algorithm that can solve the hard problem with following:
whereas indicates the time of , and queries, respectively.
Proof.
Let be an attacker who attacks on protocol and wins the game with an advantage of . A challenger C is constructed, who solves problem instance for some unknown and , to decide if holds or not. All the hash functions of the proposed protocol are simulated as random oracles, and C established a hash list which is initialized as an empty list. Message (a = 1,2,3,4) indicates the messages that are being transmitted by the entities during protocol’s execution. C chooses randomly and publishes ; C also sets identities , for the user and fog-node. C runs the proposed protocol and answers the following queries of oracle. □
Send query: initiates an active attack and using different queries forwards the messages. Four different queries are available to , which are as follows:
- (: After getting the query, C initiates a new session and returns the login message produced by the user. Particularly, C chooses and randomly and computes Moreover, C returns to , and is set to an expecting state.
- (): After getting this query, C first breaks the into picks , and computes , , . Then C returns to and sets to an expecting state.
- (): After getting this query, C breaks the into and computes and and checks either and holds or not. If not, query is rejected by C and returns nothing. Else, C picks and calculates and sends to and instance terminates.
- (): After getting this query, it is assumed that is in an expecting state, and determines if holds or not, If not, then C rejects query otherwise C returns back to and fog instance terminates.
- (): After getting this send query, it is supposed that is in an expecting state, C breaks into which verifies . If this condition does not hold then C rejects query and returns nothing. Otherwise, C calculates and the instance of the client is accepted and terminated. Furthermore, are added to list.
Corrupt query: In order to obtain the long term key of user, an adversary initiates this query.
Upon receiving corrupt the information stored in the database protected with secret key x is returned.
Execute (): On this query, the execution process of proposed protocol is simulated by C after issuing the following queries.
and C returns to .
Reveal : On receiving this query, C responds with a session key if instance is accepted; otherwise, it returns ⊥.
Test query: On receiving the test query , C chooses . If , then C gives back the true session key; otherwise, it return a the random value of similar size. The above said proof is compared on sequence games . Let be the event that outputs the correct in game (a = 0,1,2,3,4).
Game: is the actual attacking game. Just like a real player, C simulates the oracle queries in this game. The chances of success in this game are quite equal to the probability that succeeds in attacking the proposed protocol and obtains the following:
Game: This game is identical to . The difference is that C maintains a list of values . C searches the related list when the hash oracle is queried. If there exists any entry, then the same value is returned; otherwise, C returns the random value and inserts it into corresponding list . It is visible and concluded here from the properties of oracle that is indistinguishable from . Therefore:
Game : This game is identical to . The difference is that in this game, the simulation of all queries will be terminated only if the following two events occur:
- Event (): Collision on the result hash queries.
- Event (): Collision on the copy of all messages ().
As per the the definition of birthday paradox:
Since a,b,c are randomly chosen, the probability of happening is , where and are upper bound of the and queries, respectively. Therefore, we have:
Game : In this game, the send query is modified. C picks an instance () and answers queries as follows:
- When initiates a ()) query, C generates and forwards to .
- When generates a query , C generates a message to .
- When initiates a query , C sets and terminates the instance.
- When initiates a query (), C sets and returns message to . It is demonstrated that if the hard assumption holds, then the difference between and is ignorable, just as follows:
If there is a differentiator which can easily differentiate and , then C can solve the problem by using a differentiator. In the case that the differentiator interacts with , then C outputs 1; otherwise, it outputs 0. So, we get , and Equation (5) holds; the can easily differentiate the true and random session key because the values used in are random. This can only be possible if the following events occur.
- Event (): queried oracle, which has probability of
- Event (): impersonates the user by forging the which posseses the verification executed by . The probability of generating a valid by is:
- Event (): successfully impersonates the fog-node by forging , which passes the verification executed by . Like Event , the probability of generating a valid by is:
Then:
Hence,
Now, it can be concluded from Theorem 1 that as much as the problem is hard, for any challenger, it is computationally challenging to break the security of the proposed protocol in polynomial time.
5. Performance Comparison
This section presents the performance analysis between the proposed ITP-AKA and related competing protocols [11,12,13,17,18] in terms of communication, computation and storage overhead.
5.1. Experimental Setup
In order to calculate the computation overhead of the proposed and related competing protocols, hash function and point multiplication are only considered as cryptographic operations. These cryptographic operations are implemented separately for user , fog-node and cloud service provider , according to their respective devices in a controlled fog-based IoT environment, to determine their time complexity. The average values of these cryptographic operations are obtained by implementing them several times under same assumptions. The cryptographic operations that have been used at the side are implemented on Arduino, while the cryptographic operations used at the and end are implemented on a cloud server and mobile device, respectively. The specifications of Arduino, the cloud server and the mobile device are listed down in Table 3.
Table 3.
Specifications of devices.
5.2. Computation Overhead
The computation overhead is computed while comparing the time required for the execution of basic cryptographic operations used in the login and authentication phase of the proposed and related protocols [11,12,13,17,18]. The notations and required execution time for each entity are listed down in Table 4, whereas the detailed comparison of computation overhead among the proposed and related protocols is shown in Table 5.
Table 4.
Cryptographic Operations used for Analysis.
Table 5.
Comparison of computation and communication overhead.
5.3. Communication Overhead
The communication overhead of the proposed ITP-AKA and related protocols [11,12,13,17] is determined on the basis of messages that are transmitted among the participating entities of respective protocols. In the proposed ITP-AKA, during the login phase, message needs bits, whereas the messages , and transmitted during the authentication and key-establishment phase need and bits, respectively. So, the accumulative communication overhead of ITP-AKA is (672 + 1344 + 1152 + 576) = 3744 bits. Furthermore, the communication overhead of all related protocols [11,12,13,17,18] is computed in the same way. The communication overheads of Jia et al. [12], Chen et al. [13], Ma et al. [11], Sahoo et al. [17] and Ever [18] are 4064 bits, 4832 bits, 4254 bits, 2560 bits and 1120 bits, respectively. The comparative analysis of communication overhead reveals that the communication overhead of proposed protocol is less than all related competing protocols except [17,18]. The complete communication overhead comparison among the proposed and related competing protocols is presented in Table 5. Table 6 demonstrates that the designed protocol provides more security and functionality features than the related competing protocols.
Table 6.
V of security and functionality.
6. Conclusions
There are numerous applications of fog computing, ranging from normal to specific. Hence, the potential for offering privacy and security with fog-driven deployment is indispensably significant. In this article, an identity based three-party authenticated and key agreement protocol is proposed for a fog-driven IoT healthcare system. The proposed protocol is efficiently resists numerous known security attacks, as shown in Section 4. The performance analysis demonstrates that the proposed scheme has great potential for deployment in real-world healthcare systems; the computation cost of our protocol is ms, which is less than the computation cost of related protocols. Similarly, the communication cost of the proposed protocol is less than the related protocols, except Sahoo et al. and Ever.
In the future, we will design a blockchain-assisted access control protocol for fog-driven IoT healthcare systems. The reason behind employing blockchain in such infrastructure is that patients’ healthcare information is highly sensitive and confidential. Therefore, data will be stored in the blocks; due to its strong integrity and immutable and tamper-resistant qualities, blockchain will restrict adversaries from modifying healthcare data. Moreover, the proposed protocol will be simulated in a broadly-recognized NS3-simulator tool to assess its performance from distinct network parameters (i.e., end-to-end delay, throughput, etc.).
Author Contributions
Conceptualization, M.R., K.M., F.A.-T., M.S.K. and C.A.; Methodology, M.R., K.M., F.A.-T. and M.S.K.; Validation, M.A.S.; Formal analysis, M.A.S.; Data curation, M.A.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Conflicts of Interest
The authors declare no conflict of interest.
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