NDLI: Input Validation for the Laplace Differential Privacy Mechanism

Content Provider	IEEE Xplore Digital Library
Author	Costea, S. Tapus, N.
Copyright Year	2015
Description	Author affiliation: Fac. of Autom. Control & Comput. Sci., Univ. Politeh. of Bucharest, Bucharest, Romania (Costea, S.; Tapus, N.)
Abstract	Privacy is an increasing concern as the number of databases containing personal information grows. Differential privacy algorithms can be used to provide safe database queries through the insertion of noise. Attackers cannot recover pieces of the initial data with certainty, but this comes at the cost of data utility. Noise insertion leads to errors, and signal to noise ratio can become an issue. In such cases, current differential privacy mechanisms cannot inform the end user that the sanitized data might not be reliable. We propose a new differential privacy algorithm that signals the user when relative errors surpass a predefined threshold. This allows users running complex differential privacy algorithms, such as sequence processing or geographical data analysis, to improve utility through better management of large errors. We prove that our algorithm satisfies differential privacy, and perform a formal analysis of its performance. Finally, we provide guidelines on how to customize behaviour to improve results.
Starting Page	469
Ending Page	474
File Size	312433
Page Count	6
File Format	PDF
e-ISBN	9781479917808
DOI	10.1109/CSCS.2015.84
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-27
Publisher Place	Romania
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Privacy Data privacy Differential privacy Sensitivity Laplace distribution Databases Noise Partitioning algorithms Security
Content Type	Text
Resource Type	Article

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