Linear-Regression on Packed Encrypted Data in the Two-Server Model

Developing machine learning models from federated training data, containing many independent samples, is an important task that can significantly enhance the potential applicability and prediction power of learned models. Since single users, like hospitals or individual labs, typically collect data-sets that do not support accurate learning with high confidence, it is desirable to combine data from several users without compromising data privacy. In this paper, we develop a privacy-preserving solution for learning a linear regression model from data collectively contributed by several parties (``data owners\u27\u27). Our protocol is based on the protocol of Giacomelli et al. (ACNS 2018) that utilized two non colluding servers and Linearly Homomorphic Encryption (LHE) to learn regularized linear regression models. Our methods use a different LHE scheme that allows us to significantly reduce both the number and runtime of homomorphic operations, as well as the total runtime complexity. Another advantage of our protocol is that the underlying LHE scheme is based on a different (and post-quantum secure) security assumption than Giacomelli et al. Our approach leverages the Chinese Remainder Theorem, and Single Instruction Multiple Data representations, to obtain our improved performance. For a 1000 x 40 linear regression task we can learn a model in a total of 3 seconds for the homomorphic operations, compared to more than 100 seconds reported in the literature. Our approach also scales up to larger feature spaces: we implemented a system that can handle a 1000 x 100 linear regression task, investing minutes of server computing time after a more significant offline pre-processing by the data owners. We intend to incorporate our protocol and implementations into a comprehensive system that can handle secure federated learning at larger scales

Efficient Privacy-Preserving Viral Strain Classification via k-mer Signatures and FHE

With the development of sequencing technologies, viral strain classification -- which is critical for many applications, including disease monitoring and control -- has become widely deployed. Typically, a lab (client) holds a viral sequence, and requests classification services from a centralized repository of labeled viral sequences (server). However, such ``classification as a service\u27\u27 raises privacy concerns. In this paper we propose a privacy-preserving viral strain classification protocol that allows the client to obtain classification services from the server, while maintaining complete privacy of the client\u27s viral strains. The privacy guarantee is against active servers, and the correctness guarantee is against passive ones. We implemented our protocol and performed extensive benchmarks, showing that it obtains almost perfect accuracy ($99.8\%$--$100\%$) and microAUC ($0.999$), and high efficiency (amortized per-sequence client and server runtimes of $4.95$ms and $0.53$ms, respectively, and $0.21$MB communication). In addition, we present an extension of our protocol that guarantees server privacy against passive clients, and provide an empirical evaluation showing that this extension provides the same high accuracy and microAUC, with amortized per sequences overhead of only a few milliseconds in client and server runtime, and 0.3MB in communication complexity. Along the way, we develop an enhanced packing technique in which two reals are packed in a single complex number, with support for homomorphic inner products of vectors of ciphertexts. We note that while similar packing techniques were used before, they only supported additions and multiplication by constants

Privacy Preserving Feature Selection for Sparse Linear Regression

Privacy-Preserving Machine Learning (PPML) provides protocols for learning and statistical analysis of data that may be distributed amongst multiple data owners (e.g., hospitals that own proprietary healthcare data), while preserving data privacy. The PPML literature includes protocols for various learning methods, including ridge regression. Ridge regression controls the $L_2$ norm of the model, but does not aim to strictly reduce the number of non-zero coefficients, namely the $L_0$ norm of the model. Reducing the number of non-zero coefficients (a form of feature selection) is important for avoiding overfitting, and for reducing the cost of using learnt models in practice. In this work, we develop a first privacy-preserving protocol for sparse linear regression under $L_0$ constraints. The protocol addresses data contributed by several data owners (e.g., hospitals). Our protocol outsources the bulk of the computation to two non-colluding servers, using homomorphic encryption as a central tool. We provide a rigorous security proof for our protocol, where security is against semi-honest adversaries controlling any number of data owners and at most one server. We implemented our protocol, and evaluated performance with nearly a million samples and up to 40 features

Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis

Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of sera allowed for the discovery of markers of disease presence, severity, and cirrhosis and the exploration of the involvement of CCL24, a chemokine with fibro-inflammatory activity. Sera from 30 healthy controls and 45 PSC patients were profiled with proximity extension assay, quantifying the expression of 2870 proteins, and used to train an elastic net model. Proteins that contributed most to the model were tested for correlation to enhanced liver fibrosis (ELF) score and used to perform pathway analysis. Statistical modeling for the presence of cirrhosis was performed with principal component analysis (PCA), and receiver operating characteristics (ROC) curves were used to assess the useability of potential biomarkers. The model successfully predicted the presence of PSC, where the top-ranked proteins were associated with cell adhesion, immune response, and inflammation, and each had an area under receiver operator characteristic (AUROC) curve greater than 0.9 for disease presence and greater than 0.8 for ELF score. Pathway analysis showed enrichment for functions associated with PSC, overlapping with pathways enriched in patients with high levels of CCL24. Patients with cirrhosis showed higher levels of CCL24. This data-driven approach to characterize PSC and its severity highlights potential serum protein biomarkers and the importance of CCL24 in the disease, implying its therapeutic potential in PSC

Covid-19 fear impact on Israeli and Maltese female “help” profession students

Background: The aim of this cross-sectional study was to examine the impact of COVID-19 fear on the well-being of Israeli and Maltese female “help” profession (e.g., social work and psychology) undergraduate students. This cross-national comparison includes factors of depression, anxiety, anger, loneliness, nervousness, substance use, eating behavior, burnout, and resilience. The study hypothesis is that country status, even with different social–cultural characteristics including religiosity, is not a significant factor associated with COVID-19 fear impact on select behavioral characteristics of female university students. Methods: A total of 453 female “help” profession students completed an online survey from January to July 2021. Various statistical methods of analysis including regression were used for this study. Results: The mean COVID-19 fear scores were the same among Israeli and Maltese students. Resilience was found to be higher among Israeli females; burnout was found to be higher among those from Malta. Substance use (i.e., tobacco, alcohol, cannabis, stimulants, or prescription drugs) in the last month was reported by 77.2% of the respondents. No significant differences were found for previous-month substance use based on country status. Regardless of country, respondents who reported more previous-month substance use had higher COVID-19 fear and burnout scores, as well as lower resilience. Due to COVID-19, most respondents (74.3%) reported deterioration of their psycho-emotional well-being in the last month; however, no significant differences were found based on country and religiosity statuses. Furthermore, no significant differences were found for eating behavior changes and weight increase based on country and religiosity statuses. Conclusion: Study findings showed the impact of COVID-19 fear on the well-being of Israeli and Maltese female “help” profession undergraduate students. This study examined only female students; however, additional research is needed to address male students and their experiences. Prevention and treatment intervention measures aimed to increase resilience and decrease burnout, including those that can be made available on campus, should be thought about by university administration personnel and student association leaders in consultation with mental health professionals.peer-reviewe

Patient-tailored Workflow Patterns from Clinical Practice Guidelines Recommendations

Abstract MobiGuide is a project devoted to the development of a patient-centric decision support system based on computerized clinical guidelines for chronic illnesses including Atrial . In this paper we describe the process o

Travel- and Community-Based Transmission of Multidrug-Resistant Shigella sonnei Lineage among International Orthodox Jewish Communities

Shigellae are sensitive indicator species for studying trends in the international transmission of antimicrobial-resistant Enterobacteriaceae. Orthodox Jewish communities (OJCs) are a known risk group for shigellosis; Shigella sonnei is cyclically epidemic in OJCs in Israel, and sporadic outbreaks occur in OJCs elsewhere. We generated whole-genome sequences for 437 isolates of S. sonnei from OJCs and non-OJCs collected over 22 years in Europe (the United Kingdom, France, and Belgium), the United States, Canada, and Israel and analyzed these within a known global genomic context. Through phylogenetic and genomic analysis, we showed that strains from outbreaks in OJCs outside of Israel are distinct from strains in the general population and relate to a single multidrug-resistant sublineage of S. sonnei that prevails in Israel. Further Bayesian phylogenetic analysis showed that this strain emerged approximately 30 years ago, demonstrating the speed at which antimicrobial drug–resistant pathogens can spread widely through geographically dispersed, but internationally connected, communities