A security machanism for statistical database
ACM Transactions on Database Systems (TODS)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Secure and private sequence comparisons
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Models and Methods for Privacy-Preserving Data Analysis and Publishing
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Advances in Discrete Tomography and Its Applications (Applied and Numerical Harmonic Analysis)
Advances in Discrete Tomography and Its Applications (Applied and Numerical Harmonic Analysis)
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Auditing and Inference Control in Statistical Databases
IEEE Transactions on Software Engineering
Special Issue On Worst-case Versus Average-case Complexity Editors' Foreword
Computational Complexity
Towards Practical Privacy for Genomic Computation
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Proceedings of the 16th ACM conference on Computer and communications security
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
Genodroid: are privacy-preserving genomic tests ready for prime time?
Proceedings of the 2012 ACM workshop on Privacy in the electronic society
Addressing the concerns of the lacks family: quantification of kin genomic privacy
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
Protecting and evaluating genomic privacy in medical tests and personalized medicine
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
Secure genomic testing with size- and position-hiding private substring matching
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
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The rapid progress of human genome studies leads to a strong demand of aggregate human DNA data (e.g, allele frequencies, test statistics, etc.), whose public dissemination, however, has been impeded by privacy concerns. Prior research shows that it is possible to identify the presence of some participants in a study from such data, and in some cases, even fully recover their DNA sequences. A critical issue, therefore, becomes how to evaluate such a risk on individual data-sets and determine when they are safe to release. In this paper, we report our research that makes the first attempt to address this issue. We first identified the space of the aggregate-data-release problem, through examining common types of aggregate data and the typical threats they are facing. Then, we performed an in-depth study on different scenarios of attacks on different types of data, which sheds light on several fundamental questions in this problem domain. Particularly, we found that attacks on aggregate data are difficult in general, as the adversary often does not have enough information and needs to solve NP-complete or NPhard problems. On the other hand, we acknowledge that the attacks can succeed under some circumstances, particularly, when the solution space of the problem is small. Based upon such an understanding, we propose a risk-scale system and a methodology to determine when to release an aggregate data-set and when not to. We also used real human-genome data to verify our findings.