MOTIVATION: Rare diseases affect millions of people worldwide, yet their genomic foundations remain poorly understood due to limited patient data and strict privacy regulations, such as the General Data Protection Regulation (GDPR) (https://gdpr.eu/tag/gdpr/) in March 2025. These restrictions can hinder the collaborative analysis of genomic data necessary for uncovering disease-causing variants. RESULTS: We present PRISM, a novel privacy-preserving framework based on fully homomorphic encryption (FHE) that facilitates rare disease variant analysis across multiple institutions without exposing sensitive genomic information. To address the challenges of centralized trust, PRISM is built upon a Threshold FHE scheme. This approach decentralizes key management across participating institutions and ensures no single entity can unilaterally decrypt sensitive data. Our method filters disease-causing variants under recessive, dominant, and de novo inheritance models entirely on encrypted data. We propose two algorithmic variants: a multiplication-intensive (MUL-IN) approach and an addition-intensive (ADD-IN) approach. The ADD-IN algorithms minimize the number of costly multiplication operations, enabling up to a 17× improvement in runtime for recessive/dominant filtering and 22× for de novo filtering, compared to MUL-IN methods. While ADD-IN produces larger ciphertexts, efficient parallelization via SIMD and multithreading allows it to handle millions of variants in reasonable time. To the best of our knowledge, this is the first study that utilizes FHE for privacy-preserving rare disease analysis across multiple inheritance models, demonstrating its practicality and scalability in a single-cloud setting. AVAILABILITY AND IMPLEMENTATION: The source code and the data used in this work can be found in https://github.com/mdppml/PRISM.git.

MOTIVATION: Generalizing machine learning models across small, high-dimensional, and heterogeneous biological datasets remains a critical challenge due to domain shifts caused by variations in data collection, population differences, and privacy constraints that restrict data sharing. Existing federated domain adaptation (FDA) approaches primarily rely on deep learning and focus on classification tasks, making them unsuitable for privacy-sensitive, small-scale regression problems in biomedical research. We introduce a privacy-preserving federated method for unsupervised domain adaptation in regression, enabling robust learning across distributed, high-dimensional datasets while maintaining full data privacy. RESULTS: Our method is the first to enable distributed training of Gaussian processes for domain adaptation, ensuring complete privacy through randomized encoding and secure aggregation. Unlike deep learning-based FDA approaches, our method is specifically designed for small-scale, high-dimensional biological data, overcoming prior limitations in scalability and generalization. We evaluate our approach on age prediction from DNA methylation data, demonstrating that it achieves performance comparable to non-private state-of-the-art methods while fully preserving data privacy. This work enables secure and effective cross-institutional collaboration in biomedical research without requiring raw data sharing. AVAILABILITY AND IMPLEMENTATION: The source code for our method is available at https://github.com/mdppml/FREDA.

OBJECTIVE: Record linkage is essential for integrating data from multiple sources with diverse applications in real-world healthcare and research. Probabilistic Privacy-Preserving Record Linkage (PPRL) enables this integration occurs, while protecting sensitive information from unauthorized access, especially when datasets lack exact identifiers. As privacy regulations evolve and multi-institutional collaborations expand globally, there is a growing demand for methods that effectively balance security, accuracy, and efficiency. However, ensuring both privacy and scalability in large-scale record linkage remains a key challenge. METHOD: This paper presents a novel and efficient PPRL method based on a secure 3-party computation (MPC) framework. Our approach allows multiple parties to compute linkage results without exposing their private inputs and significantly improves the speed of linkage process compared to existing PPRL solutions. RESULT: Our method preserves the linkage quality of a state-of-the-art (SOTA) MPC-based PPRL method while achieving up to 14 times faster performance. For example, linking a record against a database of 10,000 records takes just 8.74 s in a realistic network with 700 Mbps bandwidth and 60 ms latency, compared to 92.32 s with the SOTA method. Even on a slower internet connection with 100 Mbps bandwidth and 60 ms latency, the linkage completes in 28 s, where as the SOTA method requires 287.96 s. These results demonstrate the significant scalability and efficiency improvements of our approach. CONCLUSION: Our novel PPRL method, based on secure 3-party computation, offers an efficient and scalable solution for large-scale record linkage while ensuring privacy protection. The approach demonstrates significant performance improvements, making it a promising tool for secure data integration in privacy-sensitive sectors.

Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature offers more sophisticated and effective methods, thereby improving sample size and accuracy. However, these methods require accessing the whole dataset, which contradicts the privacy regulations when the data is distributed among multiple sources. Especially in the medical and healthcare domain, such access reveals sensitive information about patients. This study addresses privacy-preserving imputation methods for sensitive data using secure multi-party computation, enabling secure computations without revealing any party's sensitive information. In this study, we realized the mean, median, regression, and kNN imputation methods in a privacy-preserving way. We specifically target the medical and healthcare domains considering the significance of protection of the patient data, showcasing our methods on a diabetes dataset. Experiments on the diabetes dataset validated the correctness of our privacy-preserving imputation methods, yielding the largest error around$\hat{3}\times 10^{-3}$, closely matching plaintext methods. We also analyzed the scalability of our methods to varying numbers of samples, showing their applicability to real-world healthcare problems. Our analysis demonstrated that all our methods scale linearly with the number of samples. Except for kNN, the runtime of all our methods indicates that they can be utilized for large datasets.

MOTIVATION: Diagnosis and treatment decisions on genomic data have become widespread as the cost of genome sequencing decreases gradually. In this context, disease-gene association studies are of great importance. However, genomic data are very sensitive when compared to other data types and contains information about individuals and their relatives. Many studies have shown that this information can be obtained from the query-response pairs on genomic databases. In this work, we propose a method that uses secure multi-party computation to query genomic databases in a privacy-protected manner. The proposed solution privately outsources genomic data from arbitrarily many sources to the two non-colluding proxies and allows genomic databases to be safely stored in semi-honest cloud environments. It provides data privacy, query privacy and output privacy by using XOR-based sharing and unlike previous solutions, it allows queries to run efficiently on hundreds of thousands of genomic data. RESULTS: We measure the performance of our solution with parameters similar to real-world applications. It is possible to query a genomic database with 3 000 000 variants with five genomic query predicates under 400 ms. Querying 1 048 576 genomes, each containing 1 000 000 variants, for the presence of five different query variants can be achieved approximately in 6 min with a small amount of dedicated hardware and connectivity. These execution times are in the right range to enable real-world applications in medical research and healthcare. Unlike previous studies, it is possible to query multiple databases with response times fast enough for practical application. To the best of our knowledge, this is the first solution that provides this performance for querying large-scale genomic data. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/DIFUTURE/privacy-preserving-variant-queries. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Training sophisticated machine learning models usually requires many training samples. Especially in healthcare settings these samples can be very expensive, meaning that one institution alone usually does not have enough. Merging privacy-sensitive data from different sources is usually restricted by data security and data protection measures. This can lead to approaches that reduce data quality by putting noise onto the variables (e.g., in epsilon-differential privacy) or omitting certain values (e.g., for k-anonymity). Other measures based on cryptographic methods can lead to very time-consuming computations, which is especially problematic for larger multi-omics data. We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework. ESCAPED enables the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise. We have evaluated our framework on drug resistance prediction for HIV-infected people and multi-omics dimensionality reduction and clustering problems in precision medicine. In terms of execution time, our framework significantly outperforms the best-fitting existing approaches without sacrificing the performance of the algorithm. Even though we only present the benefit for kernel-based algorithms, our framework can open up new research opportunities for further machine learning models that require the dot product of vectors from multiple sources.

MOTIVATION: The use of genome data for diagnosis and treatment is becoming increasingly common. Researchers need access to as many genomes as possible to interpret the patient genome, to obtain some statistical patterns and to reveal disease-gene relationships. The sensitive information contained in the genome data and the high risk of re-identification increase the privacy and security concerns associated with sharing such data. In this article, we present an approach to identify disease-associated variants and genes while ensuring patient privacy. The proposed method uses secure multi-party computation to find disease-causing mutations under specific inheritance models without sacrificing the privacy of individuals. It discloses only variants or genes obtained as a result of the analysis. Thus, the vast majority of patient data can be kept private. RESULTS: Our prototype implementation performs analyses on thousands of genomic data in milliseconds, and the runtime scales logarithmically with the number of patients. We present the first inheritance model (recessive, dominant and compound heterozygous) based privacy-preserving analyses of genomic data to find disease-causing mutations. Furthermore, we re-implement the privacy-preserving methods (MAX, SETDIFF and INTERSECTION) proposed in a previous study. Our MAX, SETDIFF and INTERSECTION implementations are 2.5, 1122 and 341 times faster than the corresponding operations of the state-of-the-art protocol, respectively. AVAILABILITY AND IMPLEMENTATION: https://gitlab.com/DIFUTURE/privacy-preserving-genomic-diagnosis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The decreasing cost in high-throughput technologies led to a number of sequencing projects consisting of thousands of whole genomes. The paradigm shift from exome to whole genome brings a significant increase in the size of output files. Most of the existing tools which are developed to analyse exome files are not adequate for larger VCF files produced by whole genome studies. In this work we present VCF-Explorer, a variant analysis software capable of handling large files. Memory efficiency and avoiding computationally costly pre-processing step enable to carry out the analysis to be performed with ordinary computers. VCF-Explorer provides an easy to use environment where users can define various types of queries based on variant and sample genotype level annotations. VCF-Explorer can be run in different environments and computational platforms ranging from a standard laptop to a high performance server. AVAILABILITY AND IMPLEMENTATION: VCF-Explorer is freely available at: http://vcfexplorer.sourceforge.net/. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Radio Frequency Identification (RFID) protocols should have a secret updating phase in order to protect the privacy of RFID tags against tag tracing attacks. In the literature, there are many lightweight RFID authentication protocols that try to provide key updating with lightweight cryptographic primitives. In this paper, we analyze the security of two recently proposed lightweight RFID authentication protocol against desynchronization attacks. We show that secret values shared between the back-end server and any given tag can be easily desynchronized. This weakness stems from the insufficient design of these protocols.

Recently, the rapid advance in genome sequencing technology has led to production of huge amount of sensitive genomic data. However, a serious privacy challenge is confronted with increasing number of genetic tests as genomic data is the ultimate source of identity for humans. Lately, privacy threats and possible solutions regarding the undesired access to genomic data are discussed, however it is challenging to apply proposed solutions to real life problems due to the complex nature of security definitions. In this review, we have categorized pre-existing problems and corresponding solutions in more understandable and convenient way. Additionally, we have also included open privacy problems coming with each genomic data processing procedure. We believe our classification of genome associated privacy problems will pave the way for linking of real-life problems with previously proposed methods.

Abstract Because of its low cost and ease of deployment, radio frequency identification (RFID) technology offers great potential for all applications that require identification. Main obstacle for wide adoption of this technology is the concerns about its security and privacy issues. Many RFID authentication protocols have been proposed to provide desired security and privacy level for RFID systems. Recently, Benssalah et al. have proposed a chaotic map‐based RFID security protocol. In this paper, we analyze the security of this protocol and discover its vulnerabilities. We show that message generation arises some weaknesses, and this protocol is vulnerable to tracking, tag impersonation, and de‐synchronization attacks. The success probabilities of the proposed attacks are significant, and their complexities are polynomial. Furthermore, we propose an RFID authentication protocol. Our protocol utilizes the Chebyshev chaotic map hard problem and conforms to the EPCglobal Class 1 Generation 2 (EPC C1‐G2) standard. Our protocol eliminates the weaknesses of the protocol of Benssalah et al. Copyright © 2015 John Wiley & Sons, Ltd.

Many RFID authentication protocols have been proposed to provide desired security and privacy level for RFID systems. Almost all of these protocols are based on symmetric cryptography because of the limited resources of RFID tags. Recently Cheng et. Al have proposed an RFID security protocol based on chaotic maps. In this paper, we analyse the security of this protocol and discover its vulnerabilities. We firstly present a de-synchronization attack in which a passive adversary makes the shared secrets out-of-synchronization by eavesdropping just one protocol session. We secondly present a secret disclosure attack in which a passive adversary extracts secrets of a tag by eavesdropping just one protocol session. An adversary having the secrets of the tag can launch some other attacks. Finally, we propose modifications to Cheng et. Al's protocol to eliminate its vulnerabilities.

We propose a privacy-preserving authentication scheme for RFID systems with fast lookup time. Our solution is based on the use of Physically Unclonable Functions (PUFs). Although there are many proposals that addresses the security and privacy issues of RFID, the search efficiency still remains as a challenging issue. A first tree based mutual authentication scheme for RFID systems has been proposed by Molnar and Wagner to solve the search efficiency problem. The large communication overhead of this scheme has been reduced by Dimitriou performing the authentication with one message from the tag to the reader. However, tree-based schemes are vulnerable to tag compromising attack due to lack key-updating mechanism. Therefore, tree-based schemes are weak private in the Vaudenay-Model. In this paper, we present a tree-based authentication protocol for RFID systems that is destructive-private in the Vaudenay-Model. Our proposed scheme provides resistance against tag compromising attack by using PUFs as a secure storage to keep secrets of the tag.

In this paper, we analyze storage awareness RFID authentication protocol based on sparse tree structure (SAPA), which provides backward untraceability and reduces the space for storing key sequence. We discover that SAPA does not provide location and information privacy between successful authentication sessions and does not resist denial of service attacks, forward traceability, and server impersonation. We analyze the weaknesses of SAPA and propose a new RFID authentication protocol. The proposed protocol provides better protection against privacy and security threats than those before. Our proposed protocol provides tag information privacy and tag location privacy, and resists replay attacks, denial of service attacks, backward traceability, forward traceability (under an assumption), and server impersonation with an efficient keylookup. Furthermore, our protocol has the least computation and communication load on both the tag and the server side compared to other tree based protocols.

Security is one of the main issues to adopt RFID technology in daily use. Due to resource constraints of RFID systems, it is very restricted to design a private authentication protocol based on existing cryptographic functions. In this paper, we propose a new RFID authentication protocol. The proposed protocol provides better protection against privacy and security threats than those before. Our proposed protocol is resistant to server impersonation attack introduced in [17]. Former proposal assumes that the adversary should miss any reader-to-tag communication flows and claims that their protocol is secure against forward traceability only in such communication environment. We show that even under such an assumption, the former proposed protocol is not secure. Our proposed protocol is secure against forward traceability, if the adversary misses any reader-to-tag communication flows. Our protocol also has low computational load on both the tag and the server side.