In blockchain applications, ring signatures offer significant advantages, particularly in decentralized settings, safeguarding user privacy and data security. This paper presents two innovative ring signature schemes to address the space performance challenges arising from the widespread use of ring signatures in blockchain transactions. Firstly, we introduce an aggregated ring signature scheme that effectively improves the signature space from $$\mathcal {O}(m\log n)$$ to $$\mathcal {O}(\log mn)$$ and provides corresponding security proofs. Secondly, we propose a compact multi-message ring signature scheme based on the combination lock principle, enhancing signature space efficiency to $$\mathcal {O}(m+n)$$ and optimally up to $$\mathcal {O}(m+\log n)$$ . These schemes exhibit outstanding performance in privacy-centric blockchain transactions, as demonstrated through performance analysis in practical scenarios. Additionally, we introduce aggregated linkable tags, which maintain double-spending detection in blockchain transactions. These innovative solutions are poised to effectively tackle the space efficiency challenges associated with ring signatures in blockchain transactions, thereby providing robust support for privacy preservation and data security.

Encrypted medical data outsourced to cloud servers can be used for personal health certification, health monitoring, and medical research. These data are essential to support the development of the medical industry. However, the traditional peer-to-peer data-sharing paradigm can lead to data abuse by malicious data analysis centers. Moreover, the encryption used to protect users’ outsourced privacy restricts the flexibility of data retrieval. Based on the modified double trapdoor cryptosystem, we propose an authorized data retrieval scheme over aggregated encrypted medical data (ADR-AED) in cloud-assisted e-healthcare systems. In ADR-AED, patients can access and decrypt personal data and authorize the data analysis center (DAC) to retrieve corresponding data. Specifically, we design an authorized retrieval-test mechanism for a group of patients to DAC. This allows DAC to extract valuable information from a threshold number of authorized users. Additionally, each patient can flexibly retrieve fine-grained medical data in different periods and submit them to a doctor for diagnostic analysis. The security analysis and performance evaluation demonstrate the feasibility of ADR-AED in the deployment of cloud-assisted e-healthcare systems.

Cloud computing technologies rely on powerful storage services to maintain massive data for users. Sensitive data are encrypted before outsourcing, but this limits the availability of data. Public-key encryption with keyword search (PEKS) contributes to searching target encrypted data with keywords. However, existing PEKS mechanisms require to manage certificates, they are also vulnerable to adversaries equipped with quantum-computing devices. In this paper, we devise a biometric identity-based multi-keyword search (BIB-MKS) mechanism from lattices over encrypted outsourced data, which inherently resists quantum-computing attacks. Each user in BIB-MKS is identified with her/his biometric information, which could be envisioned as the public key, thereby avoiding complex certificate managements. Particularly, BIB-MKS enables a data owner to produce an index associated with a biometric identity$BID'$, such that a user with a biometric identity$BID$issues multiple keywords in a single search query and retrieves corresponding encrypted data, if and only if$BID$and$BID'$are within a certain distance of each other as judged by some metric. BIB-MKS narrows down the search scope, and improves users search experience significantly. We define the formal security model of BIB-MKS, and prove the security of BIB-MKS under this model. The performance evaluation demonstrates that BIB-MKS is practical.

Verifiable secure aggregation (VSA) is a critical procedure in federated learning (FL), where secure aggregation achieves local gradients aggregation while data confidentiality is preserved, and verifiability enables participants to verify the correctness of aggregated results returned by a central server (CS). Most of existing solutions for VSA employ cumbersome cryptographic primitives and techniques (e.g., homomorphic encryption, bilinear pairing, interactive proof systems), which impose high communication round complexity and computational costs on participants or CS. Besides, user dropouts occur commonly in cross-device FL as a result of unstable network connection, it is demanded to design particular mechanism to deal with such events. In this paper, we present a robust secure aggregation scheme with lightweight verification for FL, by utilizing Shamir’s secret sharing technique to design a random masking code to protect the confidentiality of local gradients and achieve resilience to possible user dropout. To support verifiability upon aggregation, we extend a multi-key homomorphic MAC to achieve verification over gradient vector space. We provide security analysis to show that our scheme can protect data confidentiality against collusion attacks, meanwhile ensure the verifiable results are unforgeable under the assumption pseudorandom functions exist. We implement our scheme to verify its correctness and feasibility, performance evaluation shows its advantages in terms of efficiency and functionality.

In this article, we analyze the inherent characteristic of smart grid systems, where we observe that a smart meter always generates different types of electricity consumption data for one user, and a control center (CC) always conducts an in-depth statistic analysis on these data for subsequent services. Among these data, some of them are very sensitive and should be prevented for any leakage and modification. Furthermore, due to the large number of users in a smart grid system, it is advantageous for CC to receive and process the data from different users simultaneously. To this end, we propose a verifiable privacy-preserving multi-type data aggregation scheme (VPMDA) for smart grids. VPMDA enables an aggregator gateway (AG) to aggregate encrypted multi-type data and forward the aggregated data to CC, such that CC checks the integrity of aggregated data and obtains the statistic analysis results (e.g., average, variance) on the aggregated data without learning each individual data content. We further extend VPMDA to improve the performance of verifying data integrity on CC significantly. We formally prove the security of VPMDA against various attacks. We also implement a prototype of VPMDA and conduct a comprehensive performance evaluation to demonstrate its feasibility and efficiency.

In this article, we analyze the inherent characteristics of smart grids, and point out that some electricity consumption data are very sensitive and should be encrypted. However, once the corresponding private key is compromised, the content of encrypted data would be leaked, thereby violating users' privacy. Additionally, since a control center (CC) is always required to conduct accurate statistic analysis on these data for subsequent services, it is highly demanded for CC to check the integrity of encrypted data. To this end, based on a modified Boneh-Goh-Nissim (BGN) cryptosystem, we propose a key-leakage resilient encrypted data aggregation (KLR-EDA) scheme with lightweight verification in fog-assisted smart grids. KLR-EDA enables each fog node to aggregate first-level verifiable encrypted data from smart meters in the same grid area, and forward them to the cloud server (CS) for long-term storage. Upon receiving flexible challenging list of fog nodes from CC, CS produces second-level verifiable encrypted aggregated data and returns the results to CC. KLR-EDA enables CC to check the integrity of encrypted aggregated data efficiently, and further obtain the statistic analysis results on the aggregated data without learning any information of individual user. In particular, even the private key of CC is exposed or compromised, any adversary cannot break users' privacy. We provide security analysis of KLR-EDA, and conduct performance evaluation to demonstrate its lightweight statistical analysis and verification advantages on the CC side.