Counterterrorism: Privately Clustering A Radical Social Network Data
Journal Title: Transactions on Machine Learning and Artificial Intelligence - Year 2017, Vol 5, Issue 4
Abstract
The tradeoff between the needed or essential gathering and analysis of personal data and the privacy rights of individuals is now an important requirement under any counterterrorism program. The most famous and controversial recent example is the revelation that US intelligence agencies systemically engage in “bulk collection” of civilian “metadata” detailing telephonic and other types of communication and activities, with the alleged purpose of monitoring and thwarting terrorist activity. Differential privacy provides one of the strongest privacy guarantees up to now. In this paper, we present a new provably privacypreserving algorithm able to identify and take action upon members of the targeted subpopulation. Meanwhile, avoiding compromising the privacy of the patriot subpopulation. It is a new algorithm for search methods which use a new combination of nodes social similarity and differential privacy.
Authors and Affiliations
Jamal Boujmil, N. Tagmouti
Keywords
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- EP ID EP309081
- DOI 10.14738/tmlai.54.3204
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