With the increasing prevalence of outsourcing images to cloud servers, privacy-preserving content-based image retrieval (CBIR) has attracted significant research attention. Existing privacy-preserving CBIR schemes often prioritize retrieval speed by adopting methods that provide weak privacy guarantees and low-dimensional image features, which inevitably compromises security and retrieval accuracy. Additionally, most solutions directly employ CNN models pre-trained on public datasets for feature extraction, neglecting domain adaptation problem. To address these limitations, we propose SecOIR, a secure outsourced image retrieval scheme based on deep hashing networks, which achieves provable security under the semi-honest adversary model while hiding access patterns. Furthermore, domain adaptation is resolved through fine-tuning of feature extraction models. Experimental results demonstrate that SecOIR outperforms state-of-the-art schemes by 11%-12% in accuracy under identical datasets and configurations, while maintaining practical efficiency. To achieve SecOIR, we propose two modified function secret sharing (FSS) schemes that overcome the limited compatibility of the original FSS schemes with replicated secret sharing (RSS). Then, building upon the modified FSS schemes and RSS, we design a series of efficient sub-protocols. Benchmark tests reveal that our sub-protocols surpass existing mainstream solutions in efficiency, which can also serve as independent contributions to secure multi-party computation protocol design.

BACKGROUND: Although Sanger sequencing remains widely used in human genetic disease diagnosis and livestock breeding, software packages for analyzing such data have seen little innovation over time. Determining the genotypes of tens to hundreds of loci across hundreds or thousands of samples still typically relies on manual visual confirmation with traditional software, a process that is both time-consuming and prone to error. RESULTS: We present SAGPEK, a tool that automatically identifies genotypes at target loci from hundreds to thousands of ABI-format Sanger sequencing files and directly outputs the results. SAGPEK extracts the signal intensities for A, G, C, and T bases, performs base calling, and determines each site's homozygous or heterozygous status. It then generates a primary sequence composed of the bases with the highest signal intensities and records secondary bases for heterozygous sites. Using either built-in or user-provided anchor sequences, SAGPEK maps the coordinates of target loci, reports their genotypes, and, when applicable, annotates the corresponding amino acid changes. CONCLUSIONS: SAGPEK provides an efficient, flexible, and user-friendly solution for analyzing ABI-format Sanger sequencing data, enabling simultaneous genotyping of tens of loci across hundreds of samples. Its innovation lies not in introducing new base-calling methods, but in integrating versatile functionalities-batch genotyping, customizable anchor sequences, amino acid alteration reporting, chromatogram visualization, and local execution-into a single open-source package. This makes SAGPEK well suited for applications such as human genetic disease screening, drug-resistance mutation detection, and functional mutation identification in livestock and other organisms.

The large volume of spatial feature data generated by Internet of Things (IoT) devices is increasingly utilized in business location planning (BLP) services. reverse nearest neighbor (RNN) query techniques assist BLP in achieving more efficient business decisions by identifying candidate locations that are most attractive to users. However, existing RNN query schemes face significant challenges. First, there are some issues with data security and result integrity, as cloud servers can be both untrustworthy and malicious. Second, traditional query schemes commonly assume that the data users (DUs) are fully trusted and hold the key provided by the data owner (DO, IoT device users). In practice, however, once a DU’s key is compromised, the dataset of the DO is at risk. Regarding the above issues, this article proposes a privacy-preserving spatial feature data query scheme that supports multiple users without requiring key sharing. The proposed scheme is demonstrated using RNN queries, which are highly applicable in BLP services. Specifically, we first design a Quad-Tree for indexing spatial feature data. Then, we embed replicated secret sharing (RSS) technique into distributed two trapdoors public-key cryptosystem (DT-PKC) for key sharing. And based on this, a set of secure protocols that satisfy the mutual independence of DUs are designed for computing spatial distance and feature similarity. Finally, rigorous theoretical proofs and extensive experimental evaluations ensure the security and effectiveness of the scheme.

We propose a privacy-preserving machine learning scheme based on the cloud-edge–end architecture to address issues like weak computing power of Internet of Things (IoT) terminals, poor communication quality, and heavy cloud server burdens in traditional frameworks. Edge servers aggregate and forward terminal data, relieving terminals of heavy communication tasks and undertaking part of the computing tasks, which reduces the burden on cloud servers and improves system response speed. For privacy protection, we flexibly use homomorphic encryption and secret sharing techniques, and dynamically add differential privacy noise to resist member inference attacks. Task allocation is coordinated between different layers to optimize computing overhead. Shallow model training is performed on edge servers using homomorphic encryption, while deep model training is conducted on cloud servers using secret sharing. To achieve the conversion from homomorphic ciphertext to secret sharing shares, we design a distributed decryption protocol. Experimental results show our scheme reduces computation overhead by 20%–30% compared to existing privacy-preserving machine learning schemes based on the cloud-edge–end framework, while maintaining privacy protection throughout all stages.

As data continues to grow at an unprecedented rate and informationization accelerates, concerns over data privacy have become more prominent. In image classification tasks, the challenge of insufficient labeled data is common. Transfer learning, an effective and important machine learning method, can address this issue by leveraging knowledge from the source domain to enhance performance in the target domain. However, existing privacy-preserving transfer learning schemes continue to face challenges related to low security and multiple rounds of communication. In the following works, we design a three-party privacy-preserving transfer learning protocol based on the Joint Distributed Adaptation (JDA) algorithm, which ensures malicious security under an honest majority model. To realize this protocol, we designed a series of sub-protocols for constant-round communication, including distributed solving of eigenvalues and eigenvectors based on replicated secret sharing techniques. Compared to existing work, our protocol requires fewer rounds and satisfies malicious security. We provide formal security proofs for the designed protocol and assess its performance using real datasets. Our protocol for computing the eigenvalues of matrices in a given dimension is approximately 2.5 times faster than existing methods. The results of the experiments demonstrate both the security and effectiveness of the proposed approach.

With the increasingly pervasive deployment of fog servers, fog computing extends data processing and analysis to network edges. At the same time, as the next-generation power grid, the smart grid should meet the requirements of security, efficiency, and real-time monitoring of user energy consumption. By utilizing the low-latency and distributed properties of fog computing, it can improve communication efficiency and user service satisfaction in smart grids. For the sake of providing adequate functionality for the power grid, various schemes have been proposed. Whereas, many methods are vulnerable to privacy leakage since the existence of trusted authority may increase the exposure to threats. In this paper, we propose the EPri-MDAS: an Efficient Privacy-preserving Multiple Data Aggregation Scheme without trusted authority based on the ElGamal homomorphic cryptosystem, which achieves both data integrity verification and data source authentication with the most efficient block cipher-based authenticated encryption algorithm. It performs well in energy efficiency with strong security. Especially, the proposed multidimensional aggregation statistics scheme can perform the fine-grained data analyses; it also allows for fault tolerance while protecting personal privacy. The security analysis and simulation experiments show that EPri-MDAS can satisfy the security requirements and work efficiently in the smart grid.

Although secure multi-party computation breaks down data barriers, its utility is reduced when participants have limited computation and communication resources. To make secure multi-party computation more practical, there exists an approach to distribute users' private inputs to multiple servers in a secret sharing manner, and the servers accomplish secure computation tasks through interaction. We propose a new secure computation framework that enables the detection of malicious cloud servers by introducing homomorphic MACs. We utilize pairing-based homomorphic commitments to record MACs on a bulletin board, providing public verifiability while reducing the computation burden on the cloud servers. Additionally, our framework not only supports the underlying general computation, but also prepares for various types of nontrivial high-level operations, such as comparison and bit decomposition. We design a smart payment platform enabling fair payment with the help of smart contracts to protect the rights of both data owners and cloud service providers. Compared to previous works, our framework breaks the limitations of servers being restricted to semi-honest or even honest and provides public verifiability. Performance evaluations demonstrate satisfactory computation and communication efficiency during the online phase of our system.

Online medicine diagnosis based on pathological images has been regarded as a pervasive method due to the advances in electronic healthcare and Internet of Things (IoT), however, it also causes storage and computing stress on the local IoT devices. To solve this problem, a nature way is to outsource images to cloud servers. Unfortunately, a range of security and privacy issues arise while delegating both storage and computing to the untrusted external servers. In this paper, we present a privacy-preserving feature extraction and retrieval scheme over medical images, which allows images storage and processing on two separate cloud servers. We share the images using secret sharing technology and design a set of secure two-party computation protocols between the two cloud servers. Then a privacy-preserving convolutional neural networks (CNN) framework is constructed to achieve feature extraction, classification and retrieval of images in the encrypted domain. We analyse and evaluate our scheme in terms of both security and efficiency. The results indicate that the proposed secure protocols in our scheme can significantly reduce the computation overhead while protecting the privacy of images as well as the data generated during execution on cloud servers and final results. The performance of our scheme in image feature extraction, classification and retrieval is at a similar level comparable to the scheme based on original CNN in plaintext.

Approximate pattern matching (APM) measures whether the Hamming distance between two strings is less than a threshold value. APM has been widely utilized, such as gene matching and facial recognition. Yet, the genetic data are privacy-sensitive, resulting that the owners are unwilling to share the raw data. This inspires us to explore how to securely perform APM. After revisiting threshold private set intersection (TPSI), we first propose and formalize a functionality named Boolean threshold private set intersection (BTPSI). The new proposed BTPSI primitive returns a Boolean value (0 or 1) to the user, rather than the actual elements in TPSI. We then construct a secure protocol for the BTPSI functionality with semihonest security. Besides, we first combine oblivious transfer and BTPSI to achieve the efficient construction of secure approximate pattern matching (SAPM) protocol in a semihonest model. Furthermore, we implement our SAPM protocol to demonstrate its real practicality. The performance result shows that when the text length is 2 20 ${2}^{20}$ and the pattern length is 2 10 ${2}^{10}$ , the total runtime is less than 3 s.

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.

Due to the rapid development of new technologies such as cloud computing, Internet of Things (IoT), and mobile Internet, the data volumes are exploding. Particularly, in the industrial field, a large amount of data is generated every day. How to manage and use industrial Big Data primely is a thorny challenge for every industrial enterprise manager. As an emerging form of service, cloud computing technology provides a good solution. It receives more and more attention and support due to its flexible configuration, on-demand purchase, and easy maintenance. Using cloud technology, enterprises get rid of the heavy data management work and concentrate on their main business. Although cloud technology has many advantages, there are still many problems in terms of security and privacy. To protect the confidentiality of the data, the mainstream solution is encrypting data before uploading. In order to achieve flexible access control to encrypted data, attribute-based encryption (ABE) is an outstanding candidate. At present, more and more applications are using ABE to ensure data security. However, the privacy protection issues during the key generation phase are not considered in the current ABE systems. That is to say, the key generation center (KGC) knows both of attributes and corresponding keys of each user. This problem is especially serious in the industrial big data scenario, because it will cause great damage to the business secrets of industrial enterprises. In this paper, we design a new ABE scheme that protects user’s privacy during key issuing. In our new scheme, we separate the functionality of attribute auditing and key generating to ensure that the KGC cannot know user’s attributes and that the attribute auditing center (AAC) cannot obtain the user’s secret key. This is ideal for many privacy-sensitive scenarios, such as industrial big data scenario.

Oblivious transfer is a fundamental tool in modern cryptography. In the past few years, many studies concentrate on oblivious transfer variants with more powerful functions. In this paper, the authors propose several variants of oblivious transfer in cut-and-choose scenario, providing multiple ways of transferring data in an oblivious manner. In addition, based on homomorphic encryption, the authors construct instantiations of these primitives, which can be proven secure in malicious model under ideal/real simulation paradigm and achieve the highest security level in the real world.