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Using Noisy Binary Search for Differentially Private Anomaly Detection

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Cyber Security Cryptography and Machine Learning (CSCML 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10879))

Abstract

In this paper, we study differential privacy in noisy search. This problem is connected to noisy group testing: the goal is to find a defective or anomalous item within a group using only aggregate group queries, not individual queries. Differentially private noisy group testing has the potential to be used for anomaly detection in a way that provides differential privacy to the non-anomalous individuals while still helping to allow the anomalous individuals to be located. To do this, we introduce the notion of anomaly-restricted differential privacy. We then show that noisy group testing can be used to satisfy anomaly-restricted differential privacy while still narrowing down the location of the anomalous samples, and evaluate our approach experimentally.

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Acknowledgements

This work was partially supported by NSF under award CCF-1453432, DARPA and SSC Pacific under contract N66001-15-C-4070, and DHS under award 2009-ST-061-CCI002 and contract HSHQDC-16-A-B0005/HSHQDC-16-J-00371.

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Authors and Affiliations

  1. Department of Computer Science, Rutgers University, Piscataway, NJ, USA

    Daniel M. Bittner

  2. Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA

    Anand D. Sarwate

  3. Department of Computer Science and DIMACS, Rutgers University, Piscataway, NJ, USA

    Rebecca N. Wright

Authors
  1. Daniel M. Bittner
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  2. Anand D. Sarwate
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  3. Rebecca N. Wright
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Correspondence to Daniel M. Bittner .

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Editors and Affiliations

  1. Ben-Gurion University of the Negev, Beer Sheva, Israel

    Itai Dinur

  2. Ben-Gurion University of the Negev, Beer Sheva, Israel

    Shlomi Dolev

  3. Tata Consultancy Services (India), Chennai, Tamil Nadu, India

    Sachin Lodha

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Bittner, D.M., Sarwate, A.D., Wright, R.N. (2018). Using Noisy Binary Search for Differentially Private Anomaly Detection. In: Dinur, I., Dolev, S., Lodha, S. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2018. Lecture Notes in Computer Science(), vol 10879. Springer, Cham. https://doi.org/10.1007/978-3-319-94147-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-94147-9_3

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