As Internet of Things (IoT) technology continues to advance at a rapid pace, smart devices have permeated daily life. Service providers are actively collecting copious numbers of user data, with the aim of refining machine learning models to elevate service quality and accuracy. However, this practice has sparked apprehensions amongst users concerning the privacy and safety of their personal data. Federated learning emerges as an evolution of centralized machine learning, enabling a collective training of machine learning models by multiple users on their respective devices. Crucially, this is achieved without the direct submission of data to a central server, thereby significantly mitigating the hazards associated with privacy infringements. Since the machine learning algorithms act locally in federated learning, passing just the local model back to the central server, the users’ data remain locally. However, current research work indicates that local models also include user data privacy-related components. Moreover, current privacy-preserving secure aggregation schemes either offer insufficient accuracy or need significantly high computing resources for training. In this work, we propose an efficient and secure aggregation scheme for privacy-preserving federated learning with lower computational costs, which is suitable for those weak IoT devices since the proposed scheme is robust and fault-tolerant, allowing some of the users to dynamically exit or join the system without restarting the federated learning process or triggering abnormal termination. In addition, this scheme with the property of result verification in the situation when the servers return incorrect aggregation results, which can be verified by the users. Extensive experimental evaluations, based on real-world datasets, have substantiated the high accuracy of our proposed scheme. Moreover, in comparison to existing schemes, ours significantly reduces computational and communication costs by at least 85% and 47%, respectively.

Deep neural networks (DNNs) have profoundly changed our lifeways in recent years. The cost of training a complicated DNN model is always overwhelming for most users with limited computation and storage resources. Consequently, an increasing number of people are considering to resort to a cloud for an outsourced DNN model training. However, the DNN models training process outsourced to the cloud faces privacy and security issues due to the semi-honest and malicious cloud environments. To preserve the privacy of the data and the parameters in DNN models during the outsourced training and to detect whether the models are injected with backdoors, this paper presents DeepGuard, a framework of privacy-preserving backdoor detection and identification in an outsourced cloud environment for multi-participant computation. In particular, we design a privacy-preserving reverse engineering algorithm for recovering the triggers and detecting the backdoor attacks among three cooperative but non-collusion servers. Moreover, we propose a backdoor identification algorithm adapting to single-label and multi-label attack detection. Finally, extensive experiments on the prevailing datasets such as MNIST, SVHN, and GTSRB confirm the effectiveness and efficiency of backdoor detection and identification in a privacy-preserving DNN model.

Threshold Multi-Party Private Set Intersection (TMP-PSI) is a cryptographic protocol that enables an element from the receiver’s set to be included in the intersection result if it appears in the sets of at least $t-1$ other participants, where t represents the threshold. This protocol is crucial for a variety of applications, such as anonymous electronic voting, online ride-sharing, and close-contact tracing programs. However, most existing TMP-PSI schemes are designed based on threshold homomorphic encryption, which faces significant challenges, including low computational efficiency and a high number of communication rounds. To overcome these limitations, this study introduces the Threshold Oblivious Pseudo-Random Function (tOPRF) to fulfill the requirements of threshold encryption and decryption. Additionally, we extend the concept of the Oblivious Programmable Pseudo-Random Function (OPPRF) to develop a novel cryptographic primitive termed the Partially OPPRF (P-OPPRF). This new primitive retains the critical properties of obliviousness and randomness, along with the security assurances inherited from the OPPRF, while also offering strong resistance against malicious adversaries. Leveraging this primitive, we propose the first malicious-secure TMP-PSI protocol, named QMP-PSI, specifically designed for applications like anonymous electronic voting systems. The protocol effectively counters collusion attacks among multiple parties, ensuring robust security in multi-party environments. To further enhance voting efficiency, this work presents a cloud-assisted QMP-PSI to outsource the computationally intensive phases. This ensures that the computational overhead for participants is solely dependent on the set size and statistical security parameters, thereby maintaining security while significantly reducing the computational burden on voting participants. Finally, this work validates the protocol’s performance through extensive experiments under various set sizes, participant numbers, and threshold values. The results demonstrate that the protocol surpasses existing schemes, achieving state-of-the-art (SOTA) performance in communication overhead. Notably, in small-scale voting scenarios, it exhibits exceptional performance, particularly when the threshold is small or close to the number of participants.

Data authenticated aggregation is always a significant issue for wireless sensor networks (WSNs). The marine sensors are deployed far away from the security monitoring. Secure data aggregation for marine WSNs has emerged and attracted the interest of researchers and engineers. A multi-signature enables the data aggregation through one signature to authenticate various signers on the acknowledgement of a message, which is quite fit for data authenticated aggregation marine WSNs. However, most of the previous multi-signature schemes rely on the technique of bilinear pairing involving heavy computational overhead or the management of certificates, which cannot be afforded by the marine wireless sensors. Combined with the concept of identity-based cryptography, a few pairing-free identity-based multi-signature (IBMS) schemes have been designed on the basis of the integer factorization problem. In this paper, we propose two efficient IBMS schemes that can be used to construct provably secure data authenticated aggregation protocols under the cubic residue assumption, which is equal to integer factorization. We also employ two different methods to calculate a cubic root for the cubic residue number during the signer’s private key extraction. The algorithms are quite efficient compared to the previous work, especially for the algorithms of the multi-signature generation and its verification.