Browsing by Author "Huang, Z."

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Open Access
Differential privacy with bounded priors: Reconciling utility and privacy in genome-wide association studies
(ACM, 2015-10) Tramèr, F.; Huang, Z.; Hubaux J.-P.; Ayday, Erman
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 privacy budgets, differential privacy significantly affects the expected utility. Many alternative privacy notions which aim at relaxing DP have since been proposed, with the hope of providing a better tradeoff between privacy and utility. At CCS'13, Li et al. introduced the membership privacy framework, wherein they aim at protecting against set membership disclosure by adversaries whose prior knowledge is captured by a family of probability distributions. In the context of this framework, we investigate a relaxation of DP, by considering prior distributions that capture more reasonable amounts of background knowledge. We show that for different privacy budgets, DP can be used to achieve membership privacy for various adversarial settings, thus leading to an interesting tradeoff between privacy guarantees and utility. We re-evaluate methods for releasing differentially private χ2-statistics in genome-wide association studies and show that we can achieve a higher utility than in previous works, while still guaranteeing membership privacy in a relevant adversarial setting. © 2015 ACM.
Open Access
GenoGuard: protecting genomic data against brute-force attacks
(IEEE, 2015-05) Huang, Z.; Ayday, Erman; Fellay, Jacques; Hubaux, J-P.; Juels, A.
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 aggravating the potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term, but given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called Geno Guard, for providing strong protection for genomic data both today and in the long term. Geno Guard incorporates a new theoretical framework for encryption called honey encryption (HE): it can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can be applied to messages from, unfortunately, a very restricted set of probability distributions. Therefore, Geno Guard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In Geno Guard, a potential adversary can attempt exhaustively to guess keys or passwords and decrypt via a brute-force attack. We prove that decryption under any key will yield a plausible genome sequence, and that Geno Guard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of Geno Guard. © 2015 IEEE.
Open Access
A global reference for human genetic variation
(Nature Publishing Group, 2015) Auton, A.; Abecasis, G. R.; Altshuler, D. M.; Durbin, R. M.; Bentley, D. R.; Chakravarti, A.; Clark, A. G.; Donnelly, P.; Eichler, E. E.; Flicek, P.; Gabriel, S. B.; Gibbs, R. A.; Green, E. D.; Hurles, M. E.; Knoppers, B. M.; Korbel, J. O.; Lander, E. S.; Lee, C.; Lehrach, H.; Mardis, E. R.; Marth, G. T.; McVean, G. A.; Nickerson, D. A.; Schmidt, J. P.; Sherry, S. T.; Wang, J.; Wilson, R. K.; Boerwinkle, E.; Doddapaneni, H.; Han, Y.; Korchina, V.; Kovar, C.; Lee, S.; Muzny, D.; Reid, J. G.; Zhu, Y.; Chang, Y.; Feng, Q.; Fang, X.; Guo, X.; Jian, M.; Jiang, H.; Jin, X.; Lan, T.; Li, G.; Li, J.; Li, Y.; Liu, S.; Liu, X.; Lu, Y.; Ma, X.; Tang, M.; Wang, B.; Wang, G.; Wu, H.; Wu, R.; Xu, X.; Yin, Y.; Zhang, D.; Zhang, W.; Zhao, J.; Zhao, M.; Zheng, X.; Gupta, N.; Gharani, N.; Toji, L. H.; Gerry, N. P.; Resch, A. M.; Barker, J.; Clarke, L.; Gil, L.; Hunt, S. E.; Kelman, G.; Kulesha, E.; Leinonen, R.; McLaren, W. M.; Radhakrishnan, R.; Roa, A.; Smirnov, D.; Smith, R. E.; Streeter, I.; Thormann, A.; Toneva, I.; Vaughan, B.; Zheng-Bradley, X.; Grocock, R.; Humphray, S.; James, T.; Kingsbury, Z.; Sudbrak, R.; Albrecht, M. W.; Amstislavskiy, V. S.; Borodina, T. A.; Lienhard, M.; Mertes, F.; Sultan, M.; Timmermann, B.; Yaspo, Marie-Laure; Fulton, L.; Ananiev, V.; Belaia, Z.; Beloslyudtsev, D.; Bouk, N.; Chen, C.; Church, D.; Cohen, R.; Cook, C.; Garner, J.; Hefferon, T.; Kimelman, M.; Liu, C.; Lopez, J.; Meric, P.; O'Sullivan, C.; Ostapchuk, Y.; Phan, L.; Ponomarov, S.; Schneider, V.; Shekhtman, E.; Sirotkin, K.; Slotta, D.; Zhang, H.; Balasubramaniam, S.; Burton, J.; Danecek, P.; Keane, T. M.; Kolb-Kokocinski, A.; McCarthy, S.; Stalker, J.; Quail, M.; Davies, C. J.; Gollub, J.; Webster, T.; Wong, B.; Zhan, Y.; Campbell, C. L.; Kong, Y.; Marcketta, A.; Yu, F.; Antunes, L.; Bainbridge, M.; Sabo, A.; Huang, Z.; Coin, L. J. M.; Fang, L.; Li, Q.; Li, Z.; Lin, H.; Liu, B.; Luo, R.; Shao, H.; Xie, Y.; Ye, C.; Yu, C.; Zhang, F.; Zheng, H.; Zhu, H.; Alkan, C.; Dal, E.; Kahveci, F.; Garrison, E. P.; Kural, D.; Lee, W. P.; Leong, W. F.; Stromberg, M.; Ward, A. N.; Wu, J.; Zhang, M.; Daly, M. J.; DePristo, M. A.; Handsaker, R. E.; Banks, E.; Bhatia, G.; Del Angel, G.; Genovese, G.; Li, H.; Kashin, S.; McCarroll, S. A.; Nemesh, J. C.; Poplin, R. E.; Yoon, S. C.; Lihm, J.; Makarov, V.; Gottipati, S.; Keinan, A.; Rodriguez-Flores, J. L.; Rausch, T.; Fritz, M. H.; Stütz, A. M.; Beal, K.; Datta, A.; Herrero, J.; Ritchie, G. R. S.; Zerbino, D.; Sabeti, P. C.; Shlyakhter, I.; Schaffner, S. F.; Vitti, J.; Cooper, D. N.; Ball, E. V.; Stenson, P. D.; Barnes, B.; Bauer, M.; Cheetham, R. K.; Cox, A.; Eberle, M.; Kahn, S.; Murray, L.; Peden, J.; Shaw, R.; Kenny, E. E.; Batzer, M. A.; Konkel, M. K.; Walker, J. A.; MacArthur, D. G.; Lek, M.; Herwig, R.; Ding, L.; Koboldt, D. C.; Larson, D.; Ye, K.; Gravel, S.; Swaroop, A.; Chew, E.; Lappalainen, T.; Erlich, Y.; Gymrek, M.; Willems, T. F.; Simpson, J. T.; Shriver, M. D.; Rosenfeld, J. A.; Bustamante, C. D.; Montgomery, S. B.; De La Vega, F. M.; Byrnes, J. K.; Carroll, A. W.; DeGorter, M. K.; Lacroute, P.; Maples, B. K.; Martin, A. R.; Moreno-Estrada, A.; Shringarpure, S. S.; Zakharia, F.; Halperin, E.; Baran, Y.; Cerveira, E.; Hwang, J.; Malhotra, A.; Plewczynski, D.; Radew, K.; Romanovitch, M.; Zhang, C.; Hyland, F. C. L.; Craig, D. W.; Christoforides, A.; Homer, N.; Izatt, T.; Kurdoglu, A. A.; Sinari, S. A.; Squire, K.; Xiao, C.; Sebat, J.; Antaki, D.; Gujral, M.; Noor, A.; Ye, K.; Burchard, E. G.; Hernandez, R. D.; Gignoux, C. R.; Haussler, D.; Katzman, S. J.; Kent, W. J.; Howie, B.; Ruiz-Linares, A.; Dermitzakis, E. T.; Devine, S. E.; Kang, H. M.; Kidd, J. M.; Blackwell, T.; Caron, S.; Chen, W.; Emery, S.; Fritsche, L.; Fuchsberger, C.; Jun, G.; Li, B.; Lyons, R.; Scheller, C.; Sidore, C.; Song, S.; Sliwerska, E.; Taliun, D.; Tan, A.; Welch, R.; Wing, M. K.; Zhan, X.; Awadalla, P.; Hodgkinson, A.; Li, Y.; Shi, X.; Quitadamo, A.; Lunter, G.; Marchini, J. L.; Myers, S.; Churchhouse, C.; Delaneau, O.; Gupta-Hinch, A.; Kretzschmar, W.; Iqbal, Z.; Mathieson, I.; Menelaou, A.; Rimmer, A.; Xifara, D. K.; Oleksyk, T. K.; Fu, Y.; Liu, X.; Xiong, M.; Jorde, L.; Witherspoon, D.; Xing, J.; Browning, B. L.; Browning, S. R.; Hormozdiari, F.; Sudmant, P. H.; Khurana, E.; Tyler-Smith, C.; Albers, C. A.; Ayub, Q.; Chen, Y.; Colonna, V.; Jostins, L.; Walter, K.; Xue, Y.; Gerstein, M. B.; Abyzov, A.; Balasubramanian, S.; Chen, J.; Clarke, D.; Fu, Y.; Harmanci, A. O.; Jin, M.; Lee, D.; Liu, J.; Mu, X. J.; Zhang, J.; Zhang, Y.; Hartl, C.; Shakir, K.; Degenhardt, J.; Meiers, S.; Raeder, B.; Casale, F. P.; Stegle, O.; Lameijer, E. W.; Hall, I.; Bafna, V.; Michaelson, J.; Gardner, E. J.; Mills, R. E.; Dayama, G.; Chen, K.; Fan, X.; Chong, Z.; Chen, T.; Chaisson, M. J.; Huddleston, J.; Malig, M.; Nelson, B. J.; Parrish, N. F.; Blackburne, B.; Lindsay, S. J.; Ning, Z.; Zhang, Y.; Lam, H.; Sisu, C.; Challis, D.; Evani, U. S.; Lu, J.; Nagaswamy, U.; Yu, J.; Li, W.; Habegger, L.; Yu, H.; Cunningham, F.; Dunham, I.; Lage, K.; Jespersen, J. B.; Horn, H.; Kim, D.; Desalle, R.; Narechania, A.; Sayres, M. A. W.; Mendez, F. L.; Poznik, G. D.; Underhill, P. A.; Mittelman, D.; Banerjee, R.; Cerezo, M.; Fitzgerald, T. W.; Louzada, S.; Massaia, A.; Yang, F.; Kalra, D.; Hale, W.; Dan, X.; Barnes, K. C.; Beiswanger, C.; Cai, H.; Cao, H.; Henn, B.; Jones, D.; Kaye, J. S.; Kent, A.; Kerasidou, A.; Mathias, R.; Ossorio, P. N.; Parker, M.; Rotimi, C. N.; Royal, C. D.; Sandoval, K.; Su, Y.; Tian, Z.; Tishkoff, S.; Via, M.; Wang, Y.; Yang, H.; Yang, L.; Zhu, J.; Bodmer, W.; Bedoya, G.; Cai, Z.; Gao, Y.; Chu, J.; Peltonen, L.; Garcia-Montero, A.; Orfao, A.; Dutil, J.; Martinez-Cruzado, J. C.; Mathias, R. A.; Hennis, A.; Watson, H.; McKenzie, C.; Qadri, F.; LaRocque, R.; Deng, X.; Asogun, D.; Folarin, O.; Happi, C.; Omoniwa, O.; Stremlau, M.; Tariyal, R.; Jallow, M.; Joof, F. S.; Corrah, T.; Rockett, K.; Kwiatkowski, D.; Kooner, J.; Hien, T. T.; Dunstan, S. J.; ThuyHang, N.; Fonnie, R.; Garry, R.; Kanneh, L.; Moses, L.; Schieffelin, J.; Grant, D. S.; Gallo, C.; Poletti, G.; Saleheen, D.; Rasheed, A.; Brooks, L. D.; Felsenfeld, A. L.; McEwen, J. E.; Vaydylevich, Y.; Duncanson, A.; Dunn, M.; Schloss, J. A.
The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies. © 2015 Macmillan Publishers Limited. All rights reserved.
Open Access
Quantifying genomic privacy via inference attack with high-order SNV correlations
(IEEE, 2015) Samani, S. S.; Huang, Z.; Ayday, Erman; Elliot, M.; Fellay, J.; Hubaux, J.-P.; Kutalik, Z.
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 identified on genomic data, and thus break the privacy expectations of individuals who contribute their genomic data to medical research, or simply share their data online. Frustrating as it is, the problem could become even worse. Existing genomic privacy breaches rely on low-order SNV (Single Nucleotide Variant) correlations. Our work shows that far more powerful attacks can be designed if high-order correlations are utilized. We corroborate this concern by making use of different SNV correlations based on various genomic data models and applying them to an inference attack on individuals' genotype data with hidden SNVs. We also show that low-order models behave very differently from real genomic data and therefore should not be relied upon for privacy-preserving solutions.