Internet of Things (IoT) facilitates intelligent interconnection and data exchange between devices. However, existing data aggregation schemes face challenges such as edge node disconnections, high computational overhead, and limited fault tolerance, which affect the reliability and efficiency of the system. To address these issues, this paper proposes a smart contract assisted and fault-tolerant data aggregation scheme without a trusted authority, named Cap. To realize it, we integrate blockchain technology and homomorphic encryption. Through smart contracts, we dynamically select and switch edge nodes to ensure that the data aggregation process continues even if some nodes go offline. Moreover, by leveraging homomorphic secret sharing, we effectively minimize communication overhead and ensure that users can exit the aggregation process without jeopardizing data integrity. This scheme provides an efficient and reliable solution for secure data aggregation in Iot environments and enhances the robustness and data security of the system. Performance evaluation show that, compared with existing schemes, Cap reduces computational and communication overhead by 30%.

Federated learning enables multiple participants to construct a distributed machine learning system coordinated by a server. Most existing solutions assume a semi-honest system, considering each participant to be honest but curious, which does not align with the complex real-world environment. In reality, servers might act maliciously by tampering with or forging aggregation results, which directly threatens the integrity of global models.. To verify the integrity of server aggregation computations while protecting the privacy of clients, this paper introduces a privacy-preserving verifiable secure aggregation scheme for federated learning networks. Initially, we construct a functional reuse private key ring generation algorithm, enabling clients to encrypt and protect their private gradients using the private key ring. Subsequently, leveraging the discrete logarithm difficulty problem, we devise a commitment protocol where clients commit to their encrypted private gradients. Upon receiving the aggregation result from the server, they collaboratively unlock the commitment, thereby verifying the aggregation result. Security analysis demonstrates that our solution effectively ensures privacy protection. We tested the performance using a Raspberry Pi as an edge computing device. Experimental data reveals that, with 100 clients, our scheme demonstrates that the additional costs for proof generation and verification computations are 39.9% and 34.1% of the existing scheme, respectively, highlighting its lightweight nature.

Federated learning (FL) allows for collaborative training without centralizing data, but concerns regarding model privacy leakage, intellectual property theft and poisoning attacks have hindered its development. To mitigate such risks, this paper proposes embedded message authentication code technology (EMAC) to integrate encryption, digital signatures, and watermark functions for model security. In EMAC, the authentication data is embedded into the model ciphertext using reversible data hiding after encryption. The marked ciphertext supports data extraction for subsequent authentication and lossless decryption for testing and training simultaneously. Based on EMAC, a novel FL with security authentication and traceability of poisoning (FL-SATP) is proposed, which integrates privacy protection, identity authentication and poisoning traceability into FL. The poisoner tracing is designed to detect and identify poisoners retrospectively based on the practical performance of trained or aggregated models, thus removing the malicious users' model and deterring poisoning behaviors. Theoretical analysis and experimental results demonstrate that FL-SATP could ensure the confidentiality of the model content, the availability of model function, and that when more than half of the users are benign, the proposed method can accurately and efficiently pinpoint all malicious poisoners in FL.

Privacy protection of biometrics-based on cloud computing is attracting increasing attention. In 2018, Zhuet al.proposed an efficient and privacy-preserving online fingerprint authentication scheme for data outsourcing e-Finga. Under the premise of ensuring user's fingerprint data privacy and message security authentication, the e-Finga scheme can provide accurate and efficient fingerprint identity authentication services. However, our analysis shows that the temporary fingerprint in this scheme uses the deterministic encryption algorithm, which has the risk of leaking the user's fingerprint characteristics. Therefore, we propose a temporary fingerprint attack method for the e-Finga scheme. Experiments demonstrate that an adversary can analyze specific secret parameters and fingerprint features when eavesdropping on a user's temporary fingerprint ciphertext. To counter the temporary fingerprint attack, we propose a secure e-fingerprint scheme– Secure e-finger that uses the learning with errors samples, which has the homomorphic addition property, to encrypt user's temporary fingerprints. Experiments show that the secure e-finger scheme can resist the temporary fingerprint attack. Compared with the unprotected e-Finga scheme, the client running time is increased by about 6% percent, the communication cost on the user side only increased by 0.3125% percent. As a result, our solution can realize secure online fingerprint authentication without losing efficiency. Single user authentication is likely to cause the problem of excessive authority. Based on the Secure e-finger scheme, we propose a threshold scheme based on biological characteristics.

Threshold proxy re-encryption (TPRE) can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies, and can also provide normal services even when certain proxy servers are paralyzed or damaged. A non-interactive identity-based TPRE (IB-TPRE) scheme over lattices is proposed which removes the public key certificates. To accomplish this scheme, Shamir’s secret sharing is employed twice, which not only effectively hides the delegator’s private key information, but also decentralizes the proxy power by splitting the re-encryption key. Robustness means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext share. This property is achieved by lattice-based fully homomorphic signatures. As a result, the whole scheme is thoroughly capable of resisting quantum attacks even when they are available. The security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard model. Two typical application scenarios, including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography, are presented.

Abstract Multi‐key fully homomorphic encryption (MKFHE) allows computations on ciphertexts encrypted by different users, which can be applied to implement secure multi‐party computing (MPC). The current NTRU‐based MKFHE has the following two drawbacks: One is that the relinearisation process during homomorphic evaluation is so complicated that the corresponding computation time is costly. The other is that a class of subfield attacks are proposed and affects the security of NTRU schemes over power‐of‐2 cyclotomic rings for large moduli q, especially for the NTRU‐based fully homomorphic encryption (FHE) schemes. In this work, an efficient MKFHE scheme is proposed over prime cyclotomic rings with fewer relinearisations, which seems a good choice because of its potential to resist a subfield attack. More specifically, the time of the relinearisation process is reduced by half in homomorphic evaluations by separating the homomorphic multiplication and the relinearisation process (implementing two homomorphic multiplication operations together before relinearisation), while in current NTRU‐type MKFHE schemes, these two processes are usually performed together. The error bound of the basic function components is re‐analysed over prime cyclotomic rings in the average case, which can be used in the error analysis of our scheme. We construct an efficient NTRU‐based single‐key FHE scheme and an efficient MKFHE scheme over prime cyclotomic rings through relinearisation and modulus‐switching techniques. The MKFHE scheme proposed has the on‐the‐fly property and has a tight ciphertext size compared with the GSW‐type and BGV‐type MKFHE schemes. An experiment shows that the homomorphic evaluation of the optimised single‐key FHE scheme proposed is 1.9 times faster than an efficient NTRU‐type MKFHE DHS16 proposed at DCC 2016.

The synchronization between two neural networks by mutual learning can be used to design the neural key exchange protocol. The critical issue is how to evaluate the synchronization without a weight vector. All existing methods have a delay in evaluating the synchronization, which affects the security of the neural key exchange. To evaluate the full synchronization of neural networks more timely and accurately, an improved method for evaluating the synchronization is proposed. First, the frequency that the two networks have the same output in previous steps is used for assessing the degree of them roughly. Second, the hash function is utilized to judge whether the two networks have achieved full synchronization precisely when the degree exceeds a given threshold. The improved method can find the full synchronization between two networks with no information other than the hash value of the weight vector. Compared with other methods, the full synchronization can be detected earlier by two communication partners which adopt the method proposed in this paper. As a result, the successful probability of geometric is reduced. Therefore, the proposed method can enhance the security of the neural exchange protocol.

Nowadays, the location privacy problem has become an important problem for the users who enjoy the location-based services (LBSs). Researchers have focused on the problem of how to protect the location privacy of user efficiently for a long time. On one hand, many achievements adopt the centralized structure in which there is an additional center server. Additionally, some other researchers adopt the distributed structure to overcome the disadvantages brought by the center server in the centralized anonymous system structure. On the other hand, the existing methods of solving the problem are always to protect the individual user’s location privacy in LBSs, without considering the user group’s location privacy. This kind of methods is not very applicable to the status of a number of users who formed a group to complete a LBS task together by collaborative computing. In order to solve the problem of location privacy protection for a user group in the untrusted mobile social networks, a location privacy protection method based on the distributed structure is discussed in this paper. In the scheme, the special homomorphic features of BGN cryptosystem are cleverly used so that it can solve the group’s three classical location service applications simultaneously, namely, group nearest neighbor query, optimal group collection point determination, and group friend’s distance query, by only one security policy. If there are k users who formed the group, it could achieve k-anonymity without exposing the coordinate of each individual user or using any anonymous areas. Furthermore, theoretical and experimental analysis proves that the proposal can efficiently protect each user’s location privacy in the group through taking full advantage of the collaborative computing and communication capabilities of the mobile terminals. It can resist the existing distance interaction attack and collusion attack and can realize the secure and efficient fine-grained controllable location privacy protection for the user group.

Multi-receiver signcryption is a new cryptographic primitive that simultaneously fulfills both the functions of signature and multi-receiver encryption. Generalized Multi-Receiver signcryption can provide authenticity or confidentiality separately under specific inputs. Based on ECDSA, a generalized Multi-Receiver signcryption scheme is designed. It will be equivalent to ECDSA or multi-receiver encryption when one of party is absent. Compared with others, this scheme is based on the efficient and provably secure scheme ECDSA. It can work with three modes: signcryption, signature and encryption. A trusted party can verify the signcryption text publicly in the method of ECDSA.

Abstract Traditional signcryption is not feasible for some information security scenarios, though it is a new cryptographic primitive that simultaneously fulfills both the functions of signature and encryption.Generalized signcryptionis an adaptive primitive which achieves both secrecy and authenticity or provides them respectively by a generic structure. The notions related to generalized signcryption such as syntax, correctness, and security are proposed in the paper. A practical generalized signcryption scheme ECGSC is evaluated carefully also. The formal proofs for the unforgeability and confidentiality of ECGSC in the Random Oracle model are provided. To give a solution for multiple user settings, an efficient multicast scheme is also designed. ECGSC will seamlessly switch to the Elliptic Curve Digital Signature Algorithm (ECDSA) or a provable secure asymmetric encryption scheme when recipient's keys or sender's keys are absent. Compared with other schemes, it saves 9–14% communication costs in the signcryption mode. It also saves 78–82% computational costs. Copyright © 2007 John Wiley & Sons, Ltd.