Proceedings of the 6th iDASH Privacy and Security Workshop 2017

iDASH secure genome analysis competition 2017

Authors: XiaoFeng Wang, Haixu Tang, Shuang Wang, Xiaoqian Jiang, Wenhao Wang, Diyue Bu, Lei Wang, Yicheng Jiang and Chenghong Wang

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...

Authors: Hao Chen, Ran Gilad-Bachrach, Kyoohyung Han, Zhicong Huang, Amir Jalali, Kim Laine and Kristin Lauter

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 ...

Authors: Charlotte Bonte and Frederik Vercauteren

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...

Authors: Andrey Kim, Yongsoo Song, Miran Kim, Keewoo Lee and Jung Hee Cheon

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...

Authors: Peeter Laud and Alisa Pankova

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...

Authors: Sergiu Carpov and Thibaud Tortech