Counterterrorism: Privately Clustering a Radical Social Network Data

  • Jamal Boujmil RS&GIS Lab. Dept. Telecommunications The National School for Applied Sciences of Tetuan, Tetuan, Morocco
  • N. Tagmouti RS&GIS Lab. Dept. Telecommunications The National School for Applied Sciences of Tetuan Tetuan, Morocco
  • N. Raissouni RS&GIS Lab. Dept. Telecommunications The National School for Applied Sciences of Tetuan Tetuan, Morocco
Keywords: differentail privacy, social similarity, privacy preserving


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 privacy-preserving 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.


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How to Cite
Boujmil, J., Tagmouti, N., & Raissouni, N. (2017). Counterterrorism: Privately Clustering a Radical Social Network Data. Transactions on Machine Learning and Artificial Intelligence, 5(4).
Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems