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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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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