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Algorithm Analysis and Problem Complexity Algorithms bioinformatics biology computing Computational Biology Computer Communication Networks Computer Security Confidentiality Cryptographic Protocols cryptography Data Encryption data privacy Databases DNA Electronic Health Records encryption Genetic Privacy genetics genomic privacy Genomics Humans Information Storage and Retrieval Management of Computing and Information Systems medical information systems Medical services Polymorphism Privacy privacy protection Single Nucleotide Systems and Data Security

120 entries « 1 of 8 »

2015

Wan, Zhiyu; Vorobeychik, Yevgeniy; Xia, Weiyi; Clayton, Ellen Wright; Kantarcioglu, Murat; Ganta, Ranjit; Heatherly, Raymond; Malin, Bradley

A Game Theoretic Framework for Analyzing Re-Identification Risk (Journal Article)

PLoS ONE, 10 (3), 2015.

(Abstract | Links | BibTeX | Tags: )

@article{wan_game_2015,
title = {A Game Theoretic Framework for Analyzing Re-Identification Risk},
author = { Zhiyu Wan and Yevgeniy Vorobeychik and Weiyi Xia and Ellen Wright Clayton and Murat Kantarcioglu and Ranjit Ganta and Raymond Heatherly and Bradley A. Malin},
url = {http://dx.doi.org/10.1371/journal.pone.0120592},
doi = {10.1371/journal.pone.0120592},
year = {2015},
date = {2015-01-01},
journal = {PLoS ONE},
volume = {10},
number = {3},
abstract = {Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.

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Yang, Ji-Jiang; Li, Jian-Qiang; Niu, Yu

A hybrid solution for privacy preserving medical data sharing in the cloud environment (Journal Article)

Future Generation Computer Systems, 43–44 , pp. 74–86, 2015, ISSN: 0167-739X.

(Abstract | Links | BibTeX | Tags: Cloud storage, Integrity check, Medical data sharing, privacy protection)

@article{yang_hybrid_2015,
title = {A hybrid solution for privacy preserving medical data sharing in the cloud environment},
author = { Ji-Jiang Yang and Jian-Qiang Li and Yu Niu},
url = {http://www.sciencedirect.com/science/article/pii/S0167739X14001253},
doi = {10.1016/j.future.2014.06.004},
issn = {0167-739X},
year = {2015},
date = {2015-01-01},
journal = {Future Generation Computer Systems},
volume = {43–44},
pages = {74–86},
abstract = {Storing and sharing of medical data in the cloud environment, where computing resources including storage is provided by a third party service provider, raise serious concern of individual privacy for the adoption of cloud computing technologies. Existing privacy protection researches can be classified into three categories, i.e., privacy by policy, privacy by statistics, and privacy by cryptography. However, the privacy concerns and data utilization requirements on different parts of the medical data may be quite different. The solution for medical dataset sharing in the cloud should support multiple data accessing paradigms with different privacy strengths. The statistics or cryptography technology alone cannot enforce the multiple privacy demands, which blocks their application in the real-world cloud. This paper proposes a practical solution for privacy preserving medical record sharing for cloud computing. Based on the classification of the attributes of medical records, we use vertical partition of medical dataset to achieve the consideration of different parts of medical data with different privacy concerns. It mainly includes four components, i.e., (1) vertical data partition for medical data publishing, (2) data merging for medical dataset accessing, (3) integrity checking, and (4) hybrid search across plaintext and ciphertext, where the statistical analysis and cryptography are innovatively combined together to provide multiple paradigms of balance between medical data utilization and privacy protection. A prototype system for the large scale medical data access and sharing is implemented. Extensive experiments show the effectiveness of our proposed solution.},
keywords = {Cloud storage, Integrity check, Medical data sharing, privacy protection},
pubstate = {published},
tppubtype = {article}
}

Close

Storing and sharing of medical data in the cloud environment, where computing resources including storage is provided by a third party service provider, raise serious concern of individual privacy for the adoption of cloud computing technologies. Existing privacy protection researches can be classified into three categories, i.e., privacy by policy, privacy by statistics, and privacy by cryptography. However, the privacy concerns and data utilization requirements on different parts of the medical data may be quite different. The solution for medical dataset sharing in the cloud should support multiple data accessing paradigms with different privacy strengths. The statistics or cryptography technology alone cannot enforce the multiple privacy demands, which blocks their application in the real-world cloud. This paper proposes a practical solution for privacy preserving medical record sharing for cloud computing. Based on the classification of the attributes of medical records, we use vertical partition of medical dataset to achieve the consideration of different parts of medical data with different privacy concerns. It mainly includes four components, i.e., (1) vertical data partition for medical data publishing, (2) data merging for medical dataset accessing, (3) integrity checking, and (4) hybrid search across plaintext and ciphertext, where the statistical analysis and cryptography are innovatively combined together to provide multiple paradigms of balance between medical data utilization and privacy protection. A prototype system for the large scale medical data access and sharing is implemented. Extensive experiments show the effectiveness of our proposed solution.

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Humbert, Mathias; Huguenin, Kévin; Hugonot, Joachim; Ayday, Erman; Hubaux, Jean-Pierre

De-anonymizing Genomic Databases using Phenotypic Traits (Journal Article)

PETS 2015, 2015.

(Abstract | Links | BibTeX | Tags: )

@article{humbert_-anonymizing_2015,
title = {De-anonymizing Genomic Databases using Phenotypic Traits},
author = { Mathias Humbert and Kévin Huguenin and Joachim Hugonot and Erman Ayday and Jean-Pierre Hubaux},
url = {http://infoscience.epfl.ch/record/207479},
year = {2015},
date = {2015-01-01},
journal = {PETS 2015},
abstract = {People increasingly have their genomes sequenced and some of them share their genomic data online. They do so for various purposes including finding relatives and helping ge- nomic research. An individual’s genome carries very sen- sitive private information such as its owner’s susceptibility to diseases, which could be used for discrimination. Conse- quently, genomic databases are often anonymized. However, an individual’s genotype is also linked to visible phenotypic traits, such as eye or hair color, which can be used to re- identify users in anonymized public genomic databases, thus raising severe privacy issues. For instance, an adversary can identify a target’s genome using known phenotypic traits of hers and subsequently infer her susceptibility to Alzheimer’s disease. In this paper, we quantify the extent of this threat in several scenarios by implementing de-anonymization at- tacks on a genomic database of OpenSNP users sequenced by 23andMe, based on various phenotypic traits. Our experi- mental results show that the proportion of correct match goes up to 23% with a supervised approach in a database of 50 participants. Our approach outperforms the baseline by a factor of four, in terms of the proportion of correct match, in most scenarios. We also evaluate the ability of the adver- sary to predict the individuals’ predisposition to Alzheimer’s disease, and observe that the inference error can be halved compared to the baseline. We also analyze the effect of the number of known phenotypic traits on the success rate of the attack. As progress is made in genomic research, especially for genotype-phenotype associations, the threat presented in this paper will become more serious.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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People increasingly have their genomes sequenced and some of them share their genomic data online. They do so for various purposes including finding relatives and helping ge- nomic research. An individual’s genome carries very sen- sitive private information such as its owner’s susceptibility to diseases, which could be used for discrimination. Conse- quently, genomic databases are often anonymized. However, an individual’s genotype is also linked to visible phenotypic traits, such as eye or hair color, which can be used to re- identify users in anonymized public genomic databases, thus raising severe privacy issues. For instance, an adversary can identify a target’s genome using known phenotypic traits of hers and subsequently infer her susceptibility to Alzheimer’s disease. In this paper, we quantify the extent of this threat in several scenarios by implementing de-anonymization at- tacks on a genomic database of OpenSNP users sequenced by 23andMe, based on various phenotypic traits. Our experi- mental results show that the proportion of correct match goes up to 23% with a supervised approach in a database of 50 participants. Our approach outperforms the baseline by a factor of four, in terms of the proportion of correct match, in most scenarios. We also evaluate the ability of the adver- sary to predict the individuals’ predisposition to Alzheimer’s disease, and observe that the inference error can be halved compared to the baseline. We also analyze the effect of the number of known phenotypic traits on the success rate of the attack. As progress is made in genomic research, especially for genotype-phenotype associations, the threat presented in this paper will become more serious.

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Huang, Zhicong; Ayday, Erman; Fellay, Jacques; Hubaux, Jean-Pierre; Juels, Ari

GenoGuard: Protecting Genomic Data against Brute-Force Attacks (Inproceeding)

2015.

(Abstract | Links | BibTeX | Tags: )

@inproceedings{huang_genoguard:_2015,
title = {GenoGuard: Protecting Genomic Data against Brute-Force Attacks},
author = { Zhicong Huang and Erman Ayday and Jacques Fellay and Jean-Pierre Hubaux and Ari Juels},
url = {http://infoscience.epfl.ch/record/206772},
year = {2015},
date = {2015-01-01},
abstract = {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 multidecade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called GenoGuard, for providing strong protection for genomic data both today and in the long term. GenoGuard 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, GenoGuard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In GenoGuard, a potential adversary can attempt exhaustively to guess keys or passwords and decrypt via a bruteforce attack. We prove that decryption under any key will yield a plausible genome sequence, and that GenoGuard offers an information-theoretic security guarantee against messagerecovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of GenoGuard.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

Close

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 multidecade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called GenoGuard, for providing strong protection for genomic data both today and in the long term. GenoGuard 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, GenoGuard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In GenoGuard, a potential adversary can attempt exhaustively to guess keys or passwords and decrypt via a bruteforce attack. We prove that decryption under any key will yield a plausible genome sequence, and that GenoGuard offers an information-theoretic security guarantee against messagerecovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of GenoGuard.

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Humbert, Mathias; Ayday, Erman; Hubaux, Jean-Pierre; Telenti, Amalio

Interdependent Privacy Games: The Case of Genomics (Journal Article)

2015.

(Abstract | Links | BibTeX | Tags: )

@article{humbert_interdependent_2015,
title = {Interdependent Privacy Games: The Case of Genomics},
author = { Mathias Humbert and Erman Ayday and Jean-Pierre Hubaux and Amalio Telenti},
url = {http://infoscience.epfl.ch/record/203825},
year = {2015},
date = {2015-01-01},
abstract = {Over the last few years, the vast progress in genome sequenc- ing has highly increased the availability of genomic data. Today, individ- uals can obtain their digital genomic sequences at reasonable prices from many online service providers. Individuals can store their data on per- sonal devices, reveal it on public online databases, or share it with third parties. Yet, it has been shown that genomic data is very privacy-sensitive and highly correlated between relatives. Therefore, individuals’ decisions about how to manage and secure their genomic data are crucial. People of the same family might have very different opinions about (i) how to pro- tect and (ii) whether or not to reveal their genome.We study this tension by using a game-theoretic approach. First, we model the interplay be- tween two purely-selfish family members. We also analyze how the game evolves when relatives behave altruistically. We define closed-form Nash equilibria in different settings. We then extend the game to N players by means of multi-agent in uence diagrams that enable us to efficiently com- pute Nash equilibria. Our results notably demonstrate that altruism does not always lead to a more efficient outcome in genomic-privacy games. They also show that, if the discrepancy between the genome-sharing ben- efits that players perceive is too high, they will follow opposite sharing strategies, which has a negative impact on the familial utility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

Close

Over the last few years, the vast progress in genome sequenc- ing has highly increased the availability of genomic data. Today, individ- uals can obtain their digital genomic sequences at reasonable prices from many online service providers. Individuals can store their data on per- sonal devices, reveal it on public online databases, or share it with third parties. Yet, it has been shown that genomic data is very privacy-sensitive and highly correlated between relatives. Therefore, individuals’ decisions about how to manage and secure their genomic data are crucial. People of the same family might have very different opinions about (i) how to pro- tect and (ii) whether or not to reveal their genome.We study this tension by using a game-theoretic approach. First, we model the interplay be- tween two purely-selfish family members. We also analyze how the game evolves when relatives behave altruistically. We define closed-form Nash equilibria in different settings. We then extend the game to N players by means of multi-agent in uence diagrams that enable us to efficiently com- pute Nash equilibria. Our results notably demonstrate that altruism does not always lead to a more efficient outcome in genomic-privacy games. They also show that, if the discrepancy between the genome-sharing ben- efits that players perceive is too high, they will follow opposite sharing strategies, which has a negative impact on the familial utility.

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Humbert, Mathias; Ayday, Erman; Hubaux, Jean-Pierre; Telenti, Amalio

On Non-cooperative Genomic Privacy (Inproceeding)

2015.

(Abstract | Links | BibTeX | Tags: )

@inproceedings{humbert_non-cooperative_2015,
title = {On Non-cooperative Genomic Privacy},
author = { Mathias Humbert and Erman Ayday and Jean-Pierre Hubaux and Amalio Telenti},
url = {http://infoscience.epfl.ch/record/203833},
year = {2015},
date = {2015-01-01},
abstract = {Over the last few years, the vast progress in genome sequenc- ing has highly increased the availability of genomic data. Today, individ- uals can obtain their digital genomic sequences at reasonable prices from many online service providers. Individuals can store their data on per- sonal devices, reveal it on public online databases, or share it with third parties. Yet, it has been shown that genomic data is very privacy-sensitive and highly correlated between relatives. Therefore, individuals’ decisions about how to manage and secure their genomic data are crucial. People of the same family might have very different opinions about (i) how to pro- tect and (ii) whether or not to reveal their genome.We study this tension by using a game-theoretic approach. First, we model the interplay be- tween two purely-selfish family members. We also analyze how the game evolves when relatives behave altruistically. We define closed-form Nash equilibria in different settings. We then extend the game to N players by means of multi-agent in uence diagrams that enable us to efficiently com- pute Nash equilibria. Our results notably demonstrate that altruism does not always lead to a more efficient outcome in genomic-privacy games. They also show that, if the discrepancy between the genome-sharing ben- efits that players perceive is too high, they will follow opposite sharing strategies, which has a negative impact on the familial utility.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

Close

Over the last few years, the vast progress in genome sequenc- ing has highly increased the availability of genomic data. Today, individ- uals can obtain their digital genomic sequences at reasonable prices from many online service providers. Individuals can store their data on per- sonal devices, reveal it on public online databases, or share it with third parties. Yet, it has been shown that genomic data is very privacy-sensitive and highly correlated between relatives. Therefore, individuals’ decisions about how to manage and secure their genomic data are crucial. People of the same family might have very different opinions about (i) how to pro- tect and (ii) whether or not to reveal their genome.We study this tension by using a game-theoretic approach. First, we model the interplay be- tween two purely-selfish family members. We also analyze how the game evolves when relatives behave altruistically. We define closed-form Nash equilibria in different settings. We then extend the game to N players by means of multi-agent in uence diagrams that enable us to efficiently com- pute Nash equilibria. Our results notably demonstrate that altruism does not always lead to a more efficient outcome in genomic-privacy games. They also show that, if the discrepancy between the genome-sharing ben- efits that players perceive is too high, they will follow opposite sharing strategies, which has a negative impact on the familial utility.

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Barman, Ludovic; Graini, Mohammed-Taha El; Raisaro, Jean Louis; Ayday, Erman; Hubaux, Jean-Pierre

Privacy Threats and Practical Solutions for Genetic Risk Tests (Inproceeding)

2015.

(Abstract | Links | BibTeX | Tags: )

@inproceedings{barman_privacy_2015,
title = {Privacy Threats and Practical Solutions for Genetic Risk Tests},
author = { Ludovic Barman and Mohammed-Taha El Graini and Jean Louis Raisaro and Erman Ayday and Jean-Pierre Hubaux},
url = {http://infoscience.epfl.ch/record/207435?ln=en},
year = {2015},
date = {2015-01-01},
abstract = {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 propose simple countermeasures for the generic architecture mainly used in the literature. In particular, we present and evaluate a new practical solution against a critical attack of a malicious medical center trying to actively infer raw genetic information of patients.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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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 propose simple countermeasures for the generic architecture mainly used in the literature. In particular, we present and evaluate a new practical solution against a critical attack of a malicious medical center trying to actively infer raw genetic information of patients.

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Samani, Sahel; Huang, Zhicong; Ayday, Erman; Elliot, Mark; Fellay, Jacques; Hubaux, Jean-Pierre; Kutalik, Zoltán

Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations (Inproceeding)

2nd International Workshop on Genome Privacy and Security (in conjunction with IEEE S&P; 2015), 2015.

(Abstract | Links | BibTeX | Tags: )

@inproceedings{samani_quantifying_2015,
title = {Quantifying Genomic Privacy via Inference Attack with High-Order SNV Correlations},
author = { Sahel Samani and Zhicong Huang and Erman Ayday and Mark Elliot and Jacques Fellay and Jean-Pierre Hubaux and Zoltán Kutalik},
url = {http://infoscience.epfl.ch/record/206773},
year = {2015},
date = {2015-01-01},
booktitle = {2nd International Workshop on Genome Privacy and Security (in conjunction with IEEE S&P; 2015)},
abstract = {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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

Close

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.

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Ayday, Erman; Cristofaro, Emiliano De; Hubaux, Jean-Pierre; Tsudik, Gene

Whole Genome Sequencing: Revolutionary Medicine or Privacy Nightmare? (Journal Article)

Computer, 48 (2), pp. 58–66, 2015, ISSN: 0018-9162.

(Abstract | Links | BibTeX | Tags: bioinformatics, Biomedical monitoring, computing in healthcare, computing in medicine, Diseases, DNA, Genome sequencing, Genomics, healthcare, medical computing, Patient monitoring, patient privacy, personalized medicine, Privacy, security)

@article{ayday_whole_2015,
title = {Whole Genome Sequencing: Revolutionary Medicine or Privacy Nightmare?},
author = { Erman Ayday and Emiliano De Cristofaro and Jean-Pierre Hubaux and Gene Tsudik},
doi = {10.1109/MC.2015.59},
issn = {0018-9162},
year = {2015},
date = {2015-01-01},
journal = {Computer},
volume = {48},
number = {2},
pages = {58–66},
abstract = {Whole genome sequencing will soon become affordable for many individuals, but thorny privacy and ethical issues could jeopardize its popularity and thwart the large-scale adoption of genomics in healthcare and slow potential medical advances. The Web extra at http://youtu.be/As3J9NYsbbY is an audio recording of Alf Weaver interviewing Bradley Malin and Jacques Fellay about the possibilities and challenges of whole genome sequencing.},
keywords = {bioinformatics, Biomedical monitoring, computing in healthcare, computing in medicine, Diseases, DNA, Genome sequencing, Genomics, healthcare, medical computing, Patient monitoring, patient privacy, personalized medicine, Privacy, security},
pubstate = {published},
tppubtype = {article}
}

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Whole genome sequencing will soon become affordable for many individuals, but thorny privacy and ethical issues could jeopardize its popularity and thwart the large-scale adoption of genomics in healthcare and slow potential medical advances. The Web extra at http://youtu.be/As3J9NYsbbY is an audio recording of Alf Weaver interviewing Bradley Malin and Jacques Fellay about the possibilities and challenges of whole genome sequencing.

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2014

Erlich, Yaniv; Williams, James; Glazer, David; Yocum, Kenneth; Farahany, Nita; Olson, Maynard; Narayanan, Arvind; Stein, Lincoln; Witkowski, Jan; Kain, Robert

Redefining Genomic Privacy: Trust and Empowerment (Journal Article)

PLoS Biol, 12 (11), 2014.

(Abstract | Links | BibTeX | Tags: )

@article{erlich_redefining_2014,
title = {Redefining Genomic Privacy: Trust and Empowerment},
author = { Yaniv Erlich and James B. Williams and David Glazer and Kenneth Yocum and Nita Farahany and Maynard Olson and Arvind Narayanan and Lincoln D. Stein and Jan A. Witkowski and Robert C. Kain},
url = {http://dx.doi.org/10.1371/journal.pbio.1001983},
doi = {10.1371/journal.pbio.1001983},
year = {2014},
date = {2014-11-01},
journal = {PLoS Biol},
volume = {12},
number = {11},
abstract = {Current models of protecting human subjects create a zero-sum game of privacy versus data utility. We propose shifting the paradigm to techniques that facilitate trust between researchers and participants.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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Current models of protecting human subjects create a zero-sum game of privacy versus data utility. We propose shifting the paradigm to techniques that facilitate trust between researchers and participants.

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Xie, Wei; Kantarcioglu, Murat; Bush, William; Crawford, Dana; Denny, Joshua; Heatherly, Raymond; Malin, Bradley

SecureMA: protecting participant privacy in genetic association meta-analysis (Journal Article)

Bioinformatics (Oxford, England), 2014, ISSN: 1367-4811.

(Abstract | Links | BibTeX | Tags: )

@article{xie_securema:_2014,
title = {SecureMA: protecting participant privacy in genetic association meta-analysis},
author = { Wei Xie and Murat Kantarcioglu and William S. Bush and Dana Crawford and Joshua C. Denny and Raymond Heatherly and Bradley A. Malin},
doi = {10.1093/bioinformatics/btu561},
issn = {1367-4811},
year = {2014},
date = {2014-08-01},
journal = {Bioinformatics (Oxford, England)},
abstract = {MOTIVATION: Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data. RESULTS: We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries. Availability and implementation: Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService CONTACT: b.malin@vanderbilt.edu Supplementary information: Supplementary data are available at Bioinformatics online.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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MOTIVATION: Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data. RESULTS: We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries. Availability and implementation: Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService CONTACT: b.malin@vanderbilt.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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Raisaro, Jean Louis; Ayday, Erman; Mclaren, Paul; Hubaux, Jean-Pierre; Telenti, Amalio

Privacy-Preserving HIV Pharmacogenetics: A Real Use Case of Genomic Data Protection (Inproceeding)

Amsterdam, Netherlands, 2014.

(Abstract | BibTeX | Tags: )

@inproceedings{raisaro_privacy-preserving_2014,
title = {Privacy-Preserving HIV Pharmacogenetics: A Real Use Case of Genomic Data Protection},
author = { Jean Louis Raisaro and Erman Ayday and Paul J. Mclaren and Jean-Pierre Hubaux and Amalio Telenti},
year = {2014},
date = {2014-07-01},
address = {Amsterdam, Netherlands},
abstract = {Genomics is one of the “hot topics” in the privacy research field. Recently, several solutions have been proposed to address the complex challenge of protecting individuals’ genomic data. Several of them, however, are not practical enough to be deployed in a real operational setting. In this short paper, we propose an efficient system for privacy-preserving pharmacogenetics risk testing, and we describe its successful implementation in a pilot study of the Swiss HIV Cohort.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

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Genomics is one of the “hot topics” in the privacy research field. Recently, several solutions have been proposed to address the complex challenge of protecting individuals’ genomic data. Several of them, however, are not practical enough to be deployed in a real operational setting. In this short paper, we propose an efficient system for privacy-preserving pharmacogenetics risk testing, and we describe its successful implementation in a pilot study of the Swiss HIV Cohort.

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Erlich, Yaniv; Narayanan, Arvind

Routes for breaching and protecting genetic privacy (Journal Article)

Nature Reviews Genetics, 15 (6), pp. 409–421, 2014, ISSN: 1471-0056.

(Abstract | Links | BibTeX | Tags: )

@article{erlich_routes_2014,
title = {Routes for breaching and protecting genetic privacy},
author = { Yaniv Erlich and Arvind Narayanan},
url = {http://www.nature.com/nrg/journal/v15/n6/abs/nrg3723.html},
doi = {10.1038/nrg3723},
issn = {1471-0056},
year = {2014},
date = {2014-06-01},
journal = {Nature Reviews Genetics},
volume = {15},
number = {6},
pages = {409–421},
abstract = {We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these data sets is vital for progress in biomedical research. However, a growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, indicate the underlying assumptions, and assess their technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications. View full text},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

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We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these data sets is vital for progress in biomedical research. However, a growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, indicate the underlying assumptions, and assess their technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications. View full text

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Naveed,

Hurdles for Genomic Data Usage Management (Inproceeding)

2014 IEEE Security and Privacy Workshops (SPW), pp. 44–48, 2014.

(Abstract | Links | BibTeX | Tags: bioinformatics, cryptography, data privacy, data protection, DNA, genomic data privacy protection, genomic data usage management, Genomics, medical information systems, Privacy, Sequential analysis)

@inproceedings{naveed_hurdles_2014,
title = {Hurdles for Genomic Data Usage Management},
author = { M. Naveed},
doi = {10.1109/SPW.2014.44},
year = {2014},
date = {2014-05-01},
booktitle = {2014 IEEE Security and Privacy Workshops (SPW)},
pages = {44–48},
abstract = {Our genome determines our appearance, gender, diseases, reaction to drugs, and much more. It not only contains information about us but also about our relatives, past generations, and future generations. This creates many policy and technology challenges to protect privacy and manage usage of genomic data. In this paper, we identify various features of genomic data that make its usage management very challenging and different from other types of data. We also describe some ideas about potential solutions and propose some recommendations for the usage of genomic data.},
keywords = {bioinformatics, cryptography, data privacy, data protection, DNA, genomic data privacy protection, genomic data usage management, Genomics, medical information systems, Privacy, Sequential analysis},
pubstate = {published},
tppubtype = {inproceedings}
}

Close

Our genome determines our appearance, gender, diseases, reaction to drugs, and much more. It not only contains information about us but also about our relatives, past generations, and future generations. This creates many policy and technology challenges to protect privacy and manage usage of genomic data. In this paper, we identify various features of genomic data that make its usage management very challenging and different from other types of data. We also describe some ideas about potential solutions and propose some recommendations for the usage of genomic data.

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Cristofaro, De

Genomic Privacy and the Rise of a New Research Community (Journal Article)

IEEE Security Privacy, 12 (2), pp. 80–83, 2014, ISSN: 1540-7993.

(Abstract | Links | BibTeX | Tags: bioinformatics, Consumer protection, consumer-oriented genomic tests, data privacy, digitized genomes, Diseases, ethical aspects, ethical issues, genetic features, genetics, genomic privacy, genomic sequencing, Genomics, health care, healthcare, Medical services, personal information, Privacy, sensitive information, Sequential analysis, WGS, whole genome sequencing)

@article{de_cristofaro_genomic_2014,
title = {Genomic Privacy and the Rise of a New Research Community},
author = { E. De Cristofaro},
doi = {10.1109/MSP.2014.24},
issn = {1540-7993},
year = {2014},
date = {2014-03-01},
journal = {IEEE Security Privacy},
volume = {12},
number = {2},
pages = {80–83},
abstract = {Recent breakthroughs in whole genome sequencing (WGS) have laid the foundations to improve modern healthcare and attain a better understanding of genetic features, as well as their relation to diseases. The increased affordability of WGS prompts institutions worldwide to build large datasets of digitized genomes, often obtained from donors, and make them available for different research purposes. It also enables private individuals who are motivated by medical reasons or personal curiosity to have their genome sequenced, thus breeding a novel market for consumer-oriented genomic tests. This progress, however, also raises alarming privacy and ethical issues: genomes not only uniquely and irrevocably identify their owner but also contain treasure troves of personal and sensitive information.},
keywords = {bioinformatics, Consumer protection, consumer-oriented genomic tests, data privacy, digitized genomes, Diseases, ethical aspects, ethical issues, genetic features, genetics, genomic privacy, genomic sequencing, Genomics, health care, healthcare, Medical services, personal information, Privacy, sensitive information, Sequential analysis, WGS, whole genome sequencing},
pubstate = {published},
tppubtype = {article}
}

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Recent breakthroughs in whole genome sequencing (WGS) have laid the foundations to improve modern healthcare and attain a better understanding of genetic features, as well as their relation to diseases. The increased affordability of WGS prompts institutions worldwide to build large datasets of digitized genomes, often obtained from donors, and make them available for different research purposes. It also enables private individuals who are motivated by medical reasons or personal curiosity to have their genome sequenced, thus breeding a novel market for consumer-oriented genomic tests. This progress, however, also raises alarming privacy and ethical issues: genomes not only uniquely and irrevocably identify their owner but also contain treasure troves of personal and sensitive information.

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