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Privacy vs. Efficacy in Cloud-based Threat Detection

Published: 07 November 2014 Publication History

Abstract

Advanced threats can be detected by monitoring information systems and networks, then applying advanced analytic techniques to the data thus gathered. It is natural to gather, store, and analyze this data in the Cloud, but doing so introduces significant privacy concerns. There are technologies that can protect privacy to some extent, but these technologies reduce the efficacy of threat analytics and forensics, and introduce computation and communication overhead. This talk considers the tension between privacy and efficacy in Cloud threat detection, and analyzes both pragmatic techniques such as data anonymization via deterministic encryption and differential privacy as well as interactive techniques such as private set intersection and searchable encryption, and highlights areas where further research is needed.

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  1. Privacy vs. Efficacy in Cloud-based Threat Detection

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      cover image ACM Conferences
      CCSW '14: Proceedings of the 6th edition of the ACM Workshop on Cloud Computing Security
      November 2014
      160 pages
      ISBN:9781450332392
      DOI:10.1145/2664168
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 07 November 2014

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      Author Tags

      1. cloud
      2. privacy
      3. threat monitoring

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      CCS'14
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      CCSW '14 Paper Acceptance Rate 12 of 36 submissions, 33%;
      Overall Acceptance Rate 37 of 108 submissions, 34%

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