Search Results (73)

Confidential ledgers cannot usually be pooled across entities, yet generic private federated learning does not preserve the identities that make accounting data meaningful. This paper develops Balance-Aware Federated Analytics (BAFA), a constrained federated framework that combines voucher-level differential privacy, debit–credit and period-continuity regularization, account-hierarchy smoothing, secure aggregation, and a one-sided balance-aware update correction. The formulation defines neighboring ledgers by replacement of one complete voucher, bounds the sensitivity of the released representation, model update, and compressed balance sketch, and composes one cached representation release and all round-level aggregate releases with a Rényi differential-privacy accountant that explicitly models the minimum number of non-colluding noise contributors. It also specifies period-complete aggregation for multi-line vouchers, derives the one-sided correction from a half-space projection, and gives first-order balance-safety, hierarchy-stability, convergence, and complexity results under non-IID data, clipping, privacy noise, and sketch error. Our experiments use PaySim, IEEE-CIS Fraud Detection, and UCI Online Retail transformed into accounting-style multi-entity ledgers. The reported points indicate that BAFA improves predictive utility and normalized balance consistency relative to private federated baselines while keeping membership-inference attack AUC near random guessing. The transformed-ledger evaluation is intended as controlled evidence; validation on native enterprise ledgers remains necessary. Full article

Open wireless propagation makes unmanned aerial vehicle (UAV) links vulnerable to eavesdroppers distributed over roads, building clusters, or other continuous regions. This paper proposes a low-rank threat-subspace method for cooperative secure beamforming from distributed ground transmitters to a legitimate UAV. Steering vectors sampled over one or multiple azimuth–elevation threat regions are concatenated into a training matrix, whose dominant left singular vectors compactly represent regional exposure. The legitimate steering vector is projected onto the orthogonal complement of this subspace and power-normalized. We prove global optimality of the normalized projection for every feasible retained rank, derive a leakage bound from the first discarded singular value, and introduce uncertainty padding for independently mismatched region boundaries. Simulations evaluate disconnected and volumetric regions, deterministic geometries, Rician scattering, channel and phase errors, non-colluding and colluding eavesdroppers, and covariance-reconstruction and sampled peak-leakage baselines. In the default setting, six modes retain 98% of the sector energy, and the proposed method reduces average leakage to −26.25 dB, compared with −19.41 dB for pointwise nulling and −9.89 dB for maximum-ratio transmission. Independent boundary-error tests show that interval padding stabilizes leakage at the cost of desired gain, while multi-region tests quantify the progressive increase in effective rank. The results establish both the applicability limits and the low-overhead advantages of spatial-structure-based secure beamforming. Full article

To resist collusion between two cloud providers (CPs) in cross-verifiable outsourced computation, several smart contract-based schemes have been proposed. Their key idea is to incentivize a cloud to secretly betray collusion. In multi-round task outsourcing, collusion profits increase, and the trust built between CPs fosters collusion, making existing deposit-based anti-collusion mechanisms ineffective. To solve these problems, we propose a new solution. It differs from existing works in the following ways: (1) By introducing a reputation certificate system, it fundamentally changes the existing solutions by increasing deposits in the cat-and-mouse game. The new Outsourcing contract automatically updates a CP’s reputation certificate based on its behavior, which influences further client’s willingness to delegate tasks to the CP. As the increasing profits obtained by multiple collusions are much less than the corresponding delegation profit, a rational CP will not take the risk to lose future computation tasks. (2) We introduce a public Report contract. This contract is uniquely designed to report collusion attempts before a colluder’s contract is signed. Since no binding colluder’s contract has been signed, the reporter will not face any penalties. Consequently, our approach resolves the secrecy dilemma present in existing schemes. A further feasibility study is done by executing the contracts on an Ethereum network. Then, we conducted comparisons with state-of-the-art schemes. Both theoretical and experimental comparisons show that our scheme is the most effective in combating collusion. Full article

The destructive leadership evolution model derives from the centuries-old Gresham’s Law, and it states that “bad” leaders drive out “good” leaders, but “good” leaders cannot drive out “bad” leaders when “bad” leaders are allowed to operate with impunity. Employees will therefore face a choice between becoming a colluder, a conformer, or a leaver unless top management successfully resolves the situation. This paper analyzes the situation from the employees’ perspective using Gresham’s Law as the starting point. The principal advantage of using Gresham’s Law lies in its systemic perspective: it treats corporations as complex adaptive systems and therefore makes explicit the conditions under which particular outcomes are likely to arise. The analysis itself consists of using a random choice model and Monte Carlo simulations. The study is therefore exploratory in nature, and it demonstrates that the probability of averting destructive leadership tendencies remains low unless top management intervenes deliberately. These results align with empirical results of other researchers, indicating that managers are not any more effective at curbing destructive leaders than can be predicted by a simple random choice model. Consequently, the model suggests that for most employees a rational course of action is to exit the organization except where other compelling considerations outweigh the costs of staying. Full article

Outsourcing Internet-of-Things (IoT) data and computation to cloud and fog infrastructure exposes both the data and the access-control process to integrity, confidentiality, and privacy risks. Attribute-based encryption (ABE) provides fine-grained access control but, as deployed today, suffers from single-authority bottlenecks, expensive policy updates, weak auditability, and exposure to secret-key leakage, classical primitives are additionally threatened by future quantum adversaries. This paper does not propose a new cryptographic scheme. Instead, it contributes a conceptual reference architecture that systematizes how a set of existing, standardized primitives can be composed into a single access-control framework for IoT outsourcing, and it makes the resulting design precise enough to reason about. Concretely, we (i) define a system model and a threat model covering passive, active, colluding, bounded-leakage, and harvest-now-decrypt-later quantum adversaries; (ii) instantiate each layer with a named construction decentralized multi-authority ABE, attribute-based proxy re-encryption for policy updates, a bounded leakage resilient key model, ASCON lightweight AEAD, and ML-KEM/ML-DSA post-quantum primitives, together with a permissioned, on-chain digest/off-chain payload logging layer; (iii) specify the end-to-end data flow and module interfaces; and (iv) give a goal-by-goal security rationale and an analytical evaluation based only on standardized parameter sizes and asymptotic complexity. We are explicit about what is inherited from prior work, what remains to be proven for the composed system, and that a measured prototype evaluation remains future work. The intended value of this paper is to provide a clear, composable, and honestly scoped design that subsequent implementation studies can build upon. Full article

The rapid growth of sensitive labeled data in healthcare, finance, user profiling, and commercial databases has created increasing demand for privacy-preserving collaborative data validation across different organizations. Following prior cryptographic studies, this paper uses private collaborative data cleaning (PCDC) to refer to a specific label-conflict detection task rather than general-purpose data cleaning: the goal is to identify records for which the identifiers match but the associated labels are inconsistent, without revealing the remaining private records. Existing PCDC protocols are mainly designed for direct two-party settings where data owners must remain online and participate in the main secure computation. To reduce this online burden, we propose SS-PCDC, a secret sharing-based PCDC framework in a cloud-assisted setting. Clients locally preprocess and secret-share their labeled datasets with two non-colluding cloud servers, which perform element matching, label consistency checking, and conflict detection over secret shares. Hash-based binning is used to reduce unnecessary secure comparisons. We instantiate the framework with two concrete protocols based on arithmetic secret sharing and Boolean secret sharing, respectively. We further extend exact PCDC to threshold-based fuzzy label conflict detection and propose SS-FPCDC, which reports a matched record as conflicting when the Hamming distance between its labels exceeds a public threshold. Security analyses show that the proposed protocols securely realize their corresponding ideal functionalities against a static semi-honest adversary corrupting at most one cloud server. Experimental results demonstrate the efficiency and scalability of SS-PCDC in its intended cloud-assisted setting, particularly for large datasets and longer labels. Full article

The outsourcing of machine learning tasks, such as Support Vector Machine (SVM) training, to cloud platforms poses significant security challenges, primarily concerning the confidentiality of sensitive training data and the integrity of computation results returned by potentially malicious servers. To address these challenges, this paper proposes a lightweight, privacy-preserving, and verifiable SVM training scheme designed for resource-constrained clients. Our scheme leverages a replicated secret sharing protocol to securely distribute training data and model parameters across multiple non-colluding servers, executing the entire collaborative training process in the share domain without leaking plaintext information. Furthermore, to guarantee computational correctness, we introduce a novel interval-based index point storage strategy combined with a bilinear mapping-based parameter label consistency check. This verifiable mechanism enables clients to perform sampled, lightweight audits of the cloud’s intermediate training states and final outputs. Experimental evaluations on multiple typical datasets demonstrate that the proposed scheme maintains stable classification performance while achieving an order-of-magnitude decrease in training runtime compared with existing ciphertext-based methods, offering a highly configurable trade-off among verification coverage, computational overhead, and storage cost. Full article

Gradient Boosted Decision Trees (GBDTs) are popular for their strong predictive performance. However, in domains like finance and healthcare, data are often distributed across organizations, making collaborative model training challenging due to privacy concerns. Vertical federated learning (VFL) enables such collaboration when data are split by features, but many existing methods focus on protecting raw data while exposing sensitive model information, such as gradients and Hessians—especially to the label-owning party. Techniques like Homomorphic Encryption and Secret Sharing help, but often rely on trusted or privileged parties and may still leak intermediate statistics. To address this, we propose MPC-XGB, a privacy-preserving framework for training XGBoost under VFL with an honest-but-curious threat model. It uses secure three-party computation with Replicated Secret Sharing, distributing data across non-colluding servers and performing all computations on shares. This ensures that raw data, labels, and model statistics remain hidden, while supporting both secure training and prediction. Experiments show that MPC-XGB achieves strong performance (0.93 accuracy, 0.82 AUC), comparable to that of existing methods, with improved privacy guarantees. Full article

Privacy-preserving truth discovery (PPTD) has garnered significant attention in mobile crowdsensing (MCS). However, existing research lacks sufficient privacy protection and is often vulnerable to collusion attacks among malicious participants. Moreover, incorrect data submitted by unreliable users and their weights may reduce the accuracy of truth discovery. To address these issues, this paper proposes a privacy-preserving truth discovery framework resistant to collusion and reputation fraud (TD-RCRF) that is highly resistant to collusion and reputation fraud. The scheme employs additive secret sharing to protect sensing data, weights, intermediate results, and ground truth. To screen trustworthy users who meet reputation requirements under the non-colluding dual-server model, we propose a privacy-preserving reputation verification algorithm that combines Pedersen commitment and zero-knowledge proof to verify the validity of mobile users’ reputation values. Additionally, we propose a homomorphic strategy that converts shares between multiplication and addition and use it to design a lightweight truth discovery algorithm that further improves the accuracy of the “truth” using reputation values. Security analysis proves that TD-RCRF is privacy-preserving and secure under the non-colluding dual-server assumption. Theoretical analysis and experiments show that it is practical and efficient. Full article

Secure and bandwidth-conscious transmission of model updates is a central bottleneck in distributed machine learning. Existing secure aggregation and homomorphic encryption pipelines either reveal more than the task requires or incur prohibitive computation and communication costs. We introduce a verifiable functional encryption (VFE) framework that releases only the intended linear functions of client gradients while providing end-to-end integrity and privacy guarantees under standard lattice assumptions. Our instantiation, FlowAgg-FE, combines two novel components. First, KS-IPFE, a key-splittable inner-product FE scheme, supports per-round weighted aggregation, vector packing, and on-the-fly function changes without client re-encryption; function keys are distributed across two non-colluding helpers, eliminating a single point of trust and enabling lightweight, homomorphically verifiable tags on decrypted outputs. Second, PaS-Stream is a rate-adaptive encryption-and-compression pipeline that couples sketch-based gradient compression with batched FE ciphertext streaming, ensuring unbiased aggregation in the presence of stragglers and dropouts. We further bind client-side clipping to zero-knowledge range proofs and offer an optional differentially private release layer that composes with FE to yield $(\epsilon ,\delta )$-privacy. A prototype based on LWE demonstrates practicality across cross-device and cross-silo training: client uplink is reduced by 1.9–3.4× and server CPU time by $1.6\times$ versus state-of-practice encrypted secure aggregation, with accuracy within $0.3\%$ of plaintext baselines and correctness preserved under up to $30\%$ client dropout. These results show that verifiable FE can make secure, communication-efficient gradient transmission viable, as appropriate for theme of security and privacy in distributed machine learning of the Special Issue. Full article

We consider the problem of designing a Private Information Retrieval (PIR) scheme for n files replicated on k servers that can collude and return incorrect answers. Our goal is to correctly retrieve a specific message while keeping its identity private from the database servers. We focus on minimizing download costs and propose PIR schemes with minimal download costs and the smallest file size (proportional to the number of involved servers). Motivated by the possible presence of stragglers, we extend our previous conference results and propose a scheme in which the number of participating servers may vary. Full article

We study the problem of locally encoded secure distributed batch matrix multiplication (LESDBMM), where M pairs of sources each encode their respective batches of massive matrices and distribute the generated shares to a subset of N worker nodes. Each worker node computes a response from the received shares and sends the result to a sink node, which must be able to recover all M batches of pairwise matrix products in the presence of up to S stragglers. Additionally, any set of up to X colluding workers cannot learn any information about the matrices. Based on the idea of cross-subspace (CSA) codes and CSA null shaper, we propose the first LESDBMM scheme for batch processing. When the problem reduces to the coded distributed batch matrix multiplication (CDBMM) setting where $M=1,X=0$ and every source distributes its share to all worker nodes, the proposed scheme achieves performance matching that of the cross-subspace alignment (CSA) codes for CDBMM in terms of the maximum number of tolerable stragglers, communication cost, and computational complexity. Therefore, our scheme can be viewed as a generalization of CSA codes for CDBMM to the LESDBMM setting. Full article

Federated learning has won a lot of interest in recent years, due to its capability in collaborative learning and privacy preservation. To ensure the accuracy of outsourced training tasks, task publishers prefer to assign tasks to task workers with a high reputation. However, existing reputation-based incentive mechanisms assume that task publishers are honest, and only task workers would probably behave dishonestly to pollute the federated learning model. Different from existing work, we argue that task publishers would also behave dishonestly, where they would benefit from colluding with task workers to help task workers obtain a high reputation. In this paper, we propose a collusion-resistant and reliable incentive mechanism for federated learning. First, to measure the credibility of both task publishers and task workers, we devise a novel metric named reliability. Second, we devise a new method to compute the task publisher reliability, which is obtained by computing the deviation of reputation scores given by different task publishers, i.e., low reliability is assigned to a task publisher once its deviation is far away from that of other publishers. Third, we propose a bidirectional reputation calculation method based on the basic uncertain information model to compute reputation and reputation reliability for task workers. Furthermore, by integrating an incentive mechanism, our proposed scheme not only effectively defends against collusion attacks but also ensures that only task workers with high reputation, reputation reliability, and the capability to accomplish complex tasks can win a high reward. Finally, we conduct extensive experiments to verify the efficiency and efficacy of our proposed schemes. The results demonstrate that our proposed schemes are not only collusion-resistant but also achieve 6.31% higher test accuracy compared with the state of the art on the MNIST dataset. Full article

To overcome the obstacles of maintaining covert transmissions in wireless networks employing collaborative wardens, we develop a reinforcement learning framework that jointly optimizes cooperative jamming strategies and relay selection mechanisms. The study focuses on a multi-relay-assisted two-hop network, where potential relays dynamically act as information relays or cooperative jammers to enhance covertness. A reinforcement learning-based relay selection scheme (RLRS) is employed to dynamically select optimal relays for signal forwarding and jamming; the framework simultaneously maximizes covert throughput and guarantees warden detection failure probability, subject to rigorous power budgets. Numerical simulations reveal that the developed reinforcement learning approach outperforms conventional random relay selection (RRS) across multiple performance metrics, achieving (i) higher peak covert transmission rates, (ii) lower outage probabilities, and (iii) superior adaptability to dynamic network parameters including relay density, power allocation variations, and additive white Gaussian noise (AWGN) fluctuations. These findings validate the effectiveness of reinforcement learning in optimizing relay and jammer selection for secure covert communications under colluding warden scenarios. Full article

We consider combinatorial multi-access coded caching with private caches, where users are connected to two types of caches: private caches and multi-access caches. Each user has its own private cache, while multi-access caches are connected in the same way as caches are connected in a combinatorial topology. A scheme is proposed that satisfies the following three requirements simultaneously: (a) Linear Function Retrieval (LFR), (b) content security against an eavesdropper, and (c) demand privacy against a colluding set of users. It is shown that the private caches included in this work enable the proposed scheme to provide privacy against colluding users. For the same rate, our scheme requires less total memory accessed by each user and less total system memory than the existing scheme for multi-access combinatorial topology (no private caches) in the literature. We derive a cut-set lower bound and prove optimality when $r\ge C-1$. For $r<C-1$, we show a constant gap of 5 under certain conditions. Finally, the proposed scheme is extended to a more general setup where different users are connected to different numbers of multi-access caches, and multiple users are connected to the same subset of multi-access caches. Full article