A5089763199- Cryptography and Data Security
- Privacy-Preserving Technologies in Data
- Chaos-based Image/Signal Encryption
- Coding theory and cryptography
- Advanced Steganography and Watermarking Techniques
- Cancer Genomics and Diagnostics
- Blockchain Technology Applications and Security
- Stochastic Gradient Optimization Techniques
- Cloud Data Security Solutions
- Complexity and Algorithms in Graphs
- Biometric Identification and Security
- Adversarial Robustness in Machine Learning
- Cryptographic Implementations and Security
- Ethics in Clinical Research
- Cryptography and Residue Arithmetic
- Digital Media Forensic Detection
- Epigenetics and DNA Methylation
- Internet Traffic Analysis and Secure E-voting
- Data Quality and Management
- Privacy, Security, and Data Protection
- Face and Expression Recognition
- Security in Wireless Sensor Networks
- Genomic variations and chromosomal abnormalities
- Ethics and Social Impacts of AI
- Machine Learning in Healthcare
Broad Institute
2023
Case Western Reserve University
2022
Rutgers Sexual and Reproductive Health and Rights
2022
IBM Research - Ireland
2022
Swiss Data Science Center
2020-2021
École Polytechnique Fédérale de Lausanne
2018-2021
Data Assurance and Communication Security
2021
Universidade de Vigo
2007-2017
Growing energy needs are forcing governments to look for alternative resources and ways better manage the grid load balancing. As a major initiative, many countries including United Kingdom, States, China have already started deploying smart grids. One of biggest advantages grids compared traditional is ability remotely read fine-granular measurements from each meter, which enables operators balance efficiently offer adapted time-dependent tariffs. However, collecting data also poses serious...
Abstract Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access large quantities of patient data that are typically held separately by multiple healthcare institutions. We propose FAMHE, a novel federated analytics system that, based on multiparty homomorphic encryption (MHE), enables privacy-preserving analyses distributed datasets yielding highly accurate results without revealing any intermediate data. demonstrate the...
Furthermore, the trusted party becomes a single point of failure, thus both data and model privacy could be compromised by breaches, hacking, leaks, etc.Hence, solutions originating from cryptographic community replace emulate with group computing servers.In particular, to enable privacy-preserving training NNs, several studies employ multiparty computation (MPC) techniques operate on two [83], [28], three [82], [110],[111], or four [26], [27] server models.Such approaches, however, limit...
Human Desoxyribo-Nucleic Acid (DNA) sequences offer a wealth of information that reveal, among others, predisposition to various diseases and paternity relations. The breadth personalized nature this highlights the need for privacy-preserving protocols. In paper, we present new error-resilient string searching protocol is suitable running private DNA queries. This checks if short template (e.g., describes mutation leading disease), known one party, inside sequence owned by another accounting...
The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, medical progress. Therefore, the development new solutions that can reassure clinical sites by enabling privacy-preserving sensitive data in compliance with stringent regulations (e.g., HIPAA, GDPR) now more urgent than ever. In this work, we introduce MedCo, first operational system enables group to federate and collectively protect their order share them external...
Multisite medical data sharing is critical in modern clinical practice and research. The challenge to conduct that preserves individual privacy utility. shortcomings of traditional privacy-enhancing technologies mean institutions rely upon bespoke contracts. lengthy process administration induced by these contracts increases the inefficiency may disincentivize important treatment This paper provides a synthesis between 2 novel advanced technologies-homomorphic encryption secure multiparty...
Abstract We propose and evaluate a secure-multiparty-computation (MPC) solution in the semi-honest model with dishonest majority that is based on multiparty homomorphic encryption (MHE). To support our solution, we introduce version of Brakerski-Fan-Vercauteren cryptosystem implement it an open-source library. MHE-based MPC solutions have several advantages: Their transcript public, their o~ine phase compact, circuit-evaluation procedure noninteractive. By exploiting these properties,...
Abstract Personalised medicine can improve both public and individual health by providing targeted preventative therapeutic healthcare. However, patient data must be shared between institutions across jurisdictions for the benefits of personalised to realised. Whilst protection, privacy, research ethics laws protect confidentiality safety they also may impede multisite research, particularly jurisdictions. Accordingly, we compare concept accessibility in protection seven These include...
Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing of nearby "tag" variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit genetic sharing with an untrusted service. Here, we developed secure efficient homomorphic encryption (HE) techniques. In HE-based methods, while it transit, at rest, and analysis. It only be decrypted by owner. We compared three state-of-the-art...
Abstract In this paper, we address the problem of privacy-preserving distributed learning and evaluation machine-learning models by analyzing it in widespread MapReduce abstraction that extend with privacy constraints. We design spindle (Scalable Privacy-preservINg Distributed LEarning), first system covers complete ML workflow enabling execution a cooperative gradient-descent obtained model preserving data confidentiality passive-adversary up to N −1 colluding parties. uses multiparty...
Face recognition is one of the foremost applications in computer vision, which often involves sensitive signals; privacy concerns have been raised lately and tackled by several recent privacy-preserving face approaches. Those systems either take advantage information derived from database templates or require interaction rounds between client server, so they cannot address outsourced scenarios. We present a private verification system that can be executed server without interaction, working...
Data sharing has become of primary importance in many domains such as big-data analytics, economics and medical research, but remains difficult to achieve when the data are sensitive. In fact, personal information requires individuals' unconditional consent or is often simply forbidden for privacy security reasons. this paper, we propose Drynx, a decentralized system privacy-conscious statistical analysis on distributed datasets. Drynx relies set computing nodes enable computation statistics...
Abstract Background Privacy-preserving computations on genomic data, and more generally medical is a critical path technology for innovative, life-saving research to positively equally impact the global population. It enables algorithms be securely deployed in cloud because operations encrypted databases are conducted without revealing any individual genomes. Methods secure computation have shown significant performance improvements over last several years. However, it still challenging...
Global pandemics call for large and diverse healthcare data to study various risk factors, treatment options, disease progression patterns. Despite the enormous efforts of many consortium initiatives, scientific community still lacks a secure privacy-preserving infrastructure support auditable sharing facilitate automated legally compliant federated analysis on an international scale. Existing health informatics systems do not incorporate latest progress in modern security machine learning...
Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private distributed among multiple providers while ensuring confidentiality. Our solution, SF-PCA, end-to-end secure system that preserves confidentiality both original and all intermediate results passive-adversary model with up to all-but-one colluding parties. SF-PCA jointly leverages multiparty homomorphic...
Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit scope such collaborations3. Although cryptographic tools secure computation promise enable collaborative analysis with formal privacy guarantees, approaches either are computationally impractical or do not implement current state-of-the-art methods4–6. We introduce federated (SF-GWAS), a...
Multimedia contents are inherently sensitive signals that must be protected whenever they outsourced to an untrusted environment. This problem becomes a challenge when the environment perform some processing on signals; paradigmatic example is Cloud-based signal services. Approaches based Secure Signal Processing (SSP) address this by proposing novel mechanisms for in encrypted domain and interactive secure protocols achieve goal of protecting without disclosing information convey. paper...
Training accurate and robust machine learning models requires a large amount of data that is usually scattered across silos. Sharing or centralizing the different healthcare institutions is, however, unfeasible prohibitively difficult due to privacy regulations. In this work, we address problem by using privacy-preserving federated learning-based approach, PriCell, for complex such as convolutional neural networks. PriCell relies on multiparty homomorphic encryption enables collaborative...
In recent years, the paradigm of cloud computing has gained an increasing interest from academic community as well commercial point view. The is a very appealing concept both for providers (who can benefit hiring out their extra computation and storage resources) users avoid initial investment on resources by outsourcing processes data to cloud).
ABSTRACT Using real-world evidence in biomedical research, an indispensable complement to clinical trials, requires access large quantities of patient data that are typically held separately by multiple healthcare institutions. Centralizing those for a study is often infeasible due privacy and security concerns. Federated analytics rapidly emerging as solution enabling joint analyses distributed medical across group institutions, without sharing patient-level data. However, existing...