Georgetown University

Uri Stemmer

Abhradeep Guha Thakurta

Yaacov Nissim Kobliner

Raef Bahi Youssef Bassily

Technologies are disclosed for computing heavy hitter histograms using locally private randomization. Under this strategy, 'agents' can each hold a 'type' derived from a large dictionary. By performing an algorithm, an estimate of the distribution of data can be obtained. Two algorithms implement embodiments for performing methods involving differential privacy for one or more users, and usually are run in the local model. This means that information is collected from the agents with added noise to hide the agents' individual contributions to the histogram. The result is an accurate enough estimate of the histogram for commercial or other applications relating to the data collection of one or more agents. Specifically, the proposed algorithms improve on the performance (measured in computation and memory requirements at the server and the agent, as well as communication volume) of previously solutions.

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Cambridge, MA, United States of America

Uri Stemmer