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Adaptive Anonymity via $b$-Matching
Krzysztof M Choromanski · Tony Jebara · Kui Tang

Fri Dec 06 10:10 AM -- 10:14 AM (PST) @ Harvey's Convention Center Floor, CC
The adaptive anonymity problem is formalized where each individual shares their data along with an integer value to indicate their personal level of desired privacy. This problem leads to a generalization of $k$-anonymity to the $b$-matching setting. Novel algorithms and theory are provided to implement this type of anonymity. The relaxation achieves better utility, admits theoretical privacy guarantees that are as strong, and, most importantly, accommodates a variable level of anonymity for each individual. Empirical results confirm improved utility on benchmark and social data-sets.

Author Information

Krzysztof M Choromanski (Google DeepMind Robotics)
Tony Jebara (Spotify)
Kui Tang (Columbia University)

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