Edited by Shuang Wang, Xiaoqian Jiang, XiaoFeng Wang and Haixu Tang.
Volume 11 Supplement 4
Proceedings of the 6th iDASH Privacy and Security Workshop 2017
Proceedings
Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
Orlando, FL, USA14 October 2017
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Citation: BMC Medical Genomics 2018 11(Suppl 4):85
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Logistic regression over encrypted data from fully homomorphic encryption
One of the tasks in the 2017 iDASH secure genome analysis competition was to enable training of logistic regression models over encrypted genomic data. More precisely, given a list of approximately 1500 patien...
Citation: BMC Medical Genomics 2018 11(Suppl 4):81 -
Privacy-preserving logistic regression training
Logistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea ...
Citation: BMC Medical Genomics 2018 11(Suppl 4):86 -
Logistic regression model training based on the approximate homomorphic encryption
Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain se...
Citation: BMC Medical Genomics 2018 11(Suppl 4):83 -
Privacy-preserving record linkage in large databases using secure multiparty computation
Practical applications for data analysis may require combining multiple databases belonging to different owners, such as health centers. The analysis should be performed without violating privacy of neither th...
Citation: BMC Medical Genomics 2018 11(Suppl 4):84 -
Secure top most significant genome variants search: iDASH 2017 competition
One of the 3 tracks of iDASH Privacy & Security Workshop 2017 competition was to execute a whole genome variants search on private genomic data. Particularly, the search application was to find the top most si...
Citation: BMC Medical Genomics 2018 11(Suppl 4):82
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