Now showing items 1-20 of 22

    • Can you really anonymize the donors of genomic data in today’s digital world? 

      Alser, Mohammed; Almadhoun, Nour; Nouri, Azita; Alkan, Can; Ayday, Erman (Springer, 2016-09)
      The rapid progress in genome sequencing technologies leads to availability of high amounts of genomic data. Accelerating the pace of biomedical breakthroughs and discoveries necessitates not only collecting millions of ...
    • Cryptographic solutions for genomic privacy 

      Ayday, Erman (Springer, 2016-02)
      With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists ...
    • A demonstration of privacy-preserving aggregate queries for optimal location selection 

      Eryonucu, Cihan; Ayday, Erman; Zeydan, E. (IEEE, 2018)
      In recent years, service providers, such as mobile operators providing wireless services, collected location data in enormous extent with the increase of the usages of mobile phones. Vertical businesses, such as banks, may ...
    • Differential privacy under dependent tuples—the case of genomic privacy 

      Almadhoun, Nour; Ayday, Erman; Ulusoy, Özgür (Oxford University Press, 2020-03)
      Motivation: The rapid progress in genome sequencing has led to high availability of genomic data. Studying these data can greatly help answer the key questions about disease associations and our evolution. However, due to ...
    • Differential privacy with bounded priors: Reconciling utility and privacy in genome-wide association studies 

      Tramèr, F.; Huang, Z.; Hubaux J.-P.; Ayday, Erman (ACM, 2015-10)
      Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical methods. However, recent studies have shown that for reasonable ...
    • Dynamic attribute-based privacy-preserving genomic susceptibility testing 

      Namazi, M.; Ayday, Erman; Eryonucu, Cihan; Perez-Gonzalez, F. (Association for Computing Machinery, 2019)
      Developments in the field of genomic studies have resulted in the current high availability of genomic data which, in turn, raises significant privacy concerns. As DNA information is unique and correlated among family ...
    • The effect of kinship in re-identification attacks against genomic data sharing beacons 

      Ayoz, Kerem; Ayşen, Miray; Ayday, Erman; Çiçek, A. Ercüment (NLM (Medline), 2020-12)
      Motivation: Big data era in genomics promises a breakthrough in medicine, but sharing data in a private manner limit the pace of field. Widely accepted ‘genomic data sharing beacon’ protocol provides a standardized and ...
    • Efficient quantification of profile matching risk in social networks using belief propagation 

      Halimi, A.; Ayday, Erman (Springer Science and Business Media Deutschland GmbH, 2020)
      Many individuals share their opinions (e.g., on political issues) or sensitive information about them (e.g., health status) on the internet in an anonymous way to protect their privacy. However, anonymous data sharing has ...
    • GenoGuard: protecting genomic data against brute-force attacks 

      Huang, Z.; Ayday, Erman; Fellay, Jacques; Hubaux, J-P.; Juels, A. (IEEE, 2015-05)
      Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been ...
    • An inference attack on genomic data using kinship, complex correlations, and phenotype information 

      Deznabi, Iman; Mobayen, Mohammad; Jafari, Nazanin; Taştan, Öznur; Ayday, Erman (IEEE, 2018)
      Abstract—Individuals (and their family members) share (partial) genomic data on public platforms. However, using special characteristics of genomic data, background knowledge that can be obtained from the Web, and family ...
    • Inference attacks against differentially private query results from genomic datasets including dependent tuples 

      Almadhoun, Nour; Ayday, Erman; Ulusoy, Özgür (NLM (Medline), 2020)
      Motivation: The rapid decrease in the sequencing technology costs leads to a revolution in medical research and clinical care. Today, researchers have access to large genomic datasets to study associations between variants ...
    • Key protected classification for collaborative learning 

      Sarıyıldız, Mert Bülent; Cinbiş, R. G.; Ayday, Erman (Elsevier, 2020)
      Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving ...
    • On non-cooperative genomic privacy 

      Humbert, M.; Ayday, Erman; Hubaux J.-P.; Telenti, A. (Springer, Berlin, Heidelberg, 2015)
      Over the last few years, the vast progress in genome sequencing has highly increased the availability of genomic data. Today, individuals can obtain their digital genomic sequences at reasonable prices from many online ...
    • Privacy and security in the genomic era 

      Ayday, Erman; Hubaux, Jean-Pierre (ACM, 2016-10)
      With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists ...
    • Privacy threats and practical solutions for genetic risk tests 

      Barman, L.; Elgraini, M.-T.; Raisaro, J. L.; Hubaux, J. -P.; Ayday, Erman (IEEE, 2015)
      Recently, several solutions have been proposed to address the complex challenge of protecting individuals' genetic data during personalized medicine tests. In this short paper, we analyze different privacy threats and ...
    • Privacy-preserving aggregate queries for optimal location selection 

      Yılmaz, Emre; Ferhatosmanoğlu, H.; Ayday, Erman; Aksoy, Remzi Can (IEEE, 2019)
      Today, vast amounts of location data are collected by various service providers. These location data owners have a good idea of where their users are most of the time. Other businesses also want to use this information for ...
    • A privacy-preserving solution for the bipartite ranking problem 

      Faramarzi, Noushin Salek; Ayday, Erman; Güvenir, H. Altay (IEEE, 2016-12)
      In this paper, we propose an efficient solution for the privacy-preserving of a bipartite ranking algorithm. The bipartite ranking problem can be considered as finding a function that ranks positive instances (in a dataset) ...
    • Profile matching across online social networks 

      Halimi, A.; Ayday, Erman (Springer Science and Business Media Deutschland GmbH, 2020)
      In this work, we study the privacy risk due to profile matching across online social networks (OSNs), in which anonymous profiles of OSN users are matched to their real identities using auxiliary information about them. ...
    • Quantifying genomic privacy via inference attack with high-order SNV correlations 

      Samani, S. S.; Huang, Z.; Ayday, Erman; Elliot, M.; Fellay, J.; Hubaux, J.-P.; Kutalik, Z. (IEEE, 2015)
      As genomic data becomes widely used, the problem of genomic data privacy becomes a hot interdisciplinary research topic among geneticists, bioinformaticians and security and privacy experts. Practical attacks have been ...
    • Re-identification of individuals in genomic data-sharing beacons via allele inference 

      Von Thenen, Nora; Ayday, Erman; Çicek, A. Ercüment (Oxford University Press, 2019)
      Motivation: Genomic data-sharing beacons aim to provide a secure, easy to implement and standardized interface for data-sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. Previously ...