Federated learning provides a collaborative way to build machine learning models without sharing private data. However, attackers might infer private information from model updates submitted by participants, and the aggregator might maliciously forge the final aggregation results. Federated learning still faces data privacy and aggregation integrity challenges. In this paper, we combine inference attacks and information theory to analyze the sensitivity of different bits of model parameters. We conclude that not all bits of model parameters will leak privacy. This realization inspires us to propose a novel low-expansion homomorphic aggregation scheme based on Paillier homomorphic encryption (PHE) for safeguarding participants’ data privacy. Building upon this, we develop PriVeriFL-A, a privacy-preserving and aggregation-verifiable federated learning scheme that combines homomorphic hash function and signature. To prevent collusion attacks between the aggregator and malicious participants, we further improve our PHE-based scheme into a threshold PHE-based one, named PriVeriFL-B. Compared with the privacy-preserving federated learning scheme based on classic PHE, PriVeriFL-A reduces the communication overhead to 1.65%, and the encryption/decryption computation overhead to 0.88%. Both PriVeriFL-A and PriVeriFL-B can effectively verify the integrity of the global model, while maintaining an almost negligible communication overhead for integrity verification and protecting the privacy of participants’ data.

Federated learning (FL) when deployed in an Internet of Things (IoT) ecosystem can facilitate the collaborative training of a global model involving different IoT local systems. However, there are a number of challenges in such deployments, and examples include single point of failure / attack, lack of fault tolerance, vulnerability to collusion attacks and accuracy loss. Therefore, we propose a privacy-preserving serverless FL scheme for IoT based on secure multi-party computation. Specifically, in our scheme, no central sever is required to coordinate the generation of global models. In doing so, we avoid the single point of failure / attack limitation. We also mitigate the fault tolerance limitation by using secret sharing. Finally, we provide a formal security proof that demonstrates the resilience of our scheme against collusion attacks, thereby establishing its effectiveness in achieving robust data privacy. Simulations are also implemented to show that our scheme does not suffer from accuracy loss.

Federated learning (FL) allows multiple users to collaboratively train global machine learning models by keeping their data sets local. However, the existing privacy-preserving FL schemes suffer from several limitations, e.g., loss of accuracy, high communication/computation cost, failure to support dynamic users, and insecurity against collusion attacks. To solve these limitations, we propose a lightweight privacy-preserving FL scheme based on a dual-server architecture. Our scheme involves only lightweight cryptographic operations, i.e., hash and symmetric encryption operations, and it has low communication overhead. Thus, it is computationally lightweight and round-efficient. Further, it allows users to join/quit an FL task and it is accuracy-lossless. We formally prove that our scheme remains secure even in case of collusion attacks. In particular, if an attacker colludes with one of the servers and all the users who participate in an FL task except two, the privacy of user gradients stays unviolated. The reported experimental results demonstrate that our scheme incurs only a marginal increase in total communication overhead compared to the FL scheme without any privacy protection. In terms of computation overhead, the cost per user remains stable as the number of users grows, while the cost for the server is comparable to that of the FL scheme without any privacy protection.

The privacy of Cryptocurrencies are of great concern in various fields. Researches has shown that pseudonyms, which are used in Bitcoin, only provide weak privacy. The privacy of users may be put at risk under deanonymization attacks. The exisiting schemes typically require a trusted-third party to achieve anonymity, however this usually faces a single-point fault. In addition, existing schemes suffer from high communication complexity and impracticality. This paper proposes a practical privacy-preserving mixing protocol for Bitcoin to achieve unlink-ability of input and output address of transactions. Compared to existing schemes, our protocol improves practicality. The communication complexity of our protocol is linearly related to the number of peers. Moreover, our protocol is scalable as it works not only for Bitcoin, but also for other cryptocurrencies.

Secure wildcard pattern matching (WPM) allows the pattern holder to obtain the matched positions without revealing pattern and text information about both parties. However, standard secure WPM may have limitations in practical applications, as users may prefer to have access to the actual data of the match in many scenarios. Fortunately, secure wildcard pattern matching with query (SWMQ) extends standard secure WPM by allowing the pattern holder to obtain the matched positions and the actual data, which has important applications in many scenarios, such as electronic healthcare and gene matching. This also motivates the research of SWMQ in this paper. In this study, we focus on the efficient construction of SWMQ in the semihonest adversary setting. First, we propose two new primitives, hereafter referred to as shared wildcard pattern matching (Sh-WPM) and choice-sharing oblivious transfer (CSOT). Furthermore, we propose an SWMQ protocol via Shared WPM and CSOT. In addition, we evaluate the performance of SWMQ. More specifically, the running time in local area network and wide area network settings is less than 0.4 and 2 s, respectively, when the text length is 2 16 ${2}^{16}$ and the pattern length is 2 12 ${2}^{12}$ . In fact, our evaluation results suggest that SWMQ is not only more broadly functional, but also comparable in efficiency to state-of-the-art approaches.