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­User-Centric Distributed Solutions For Privacy-Preserving Analytics

By Azer Bestavros, Andrei Lapets, Mayank Varia

Communications of the ACM, Vol. 60 No. 2, Pages 37-39
10.1145/3029603

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For over a year, a high-profile initiative spearheaded by the City of Boston and the Boston Women's Workforce Council (BWWC) strived to identify salary inequities across various employee gender and ethnic demographics at different levels of employment, from executive to entry-level positions.11 While the effort was supported by a diverse set of more than 100 employer organizations in the city—including major corporations, small businesses, and public/nonprofit organizations—it was stalled by concerns about the confidentiality of the data to be collected in order to calculate aggregate metrics.2

A key enabling technology that allowed this effort to move forward was a Web-based application (which can be seen at 100talent.org) that we designed and implemented at Boston University to support the aggregation of sensitive salary data using secure multi-party computation (MPC).8 This application was used in a first-of-its-kind collaborative effort to compute aggregate payroll analytics without revealing the individual datasets of contributing organizations. This deployment of MPC, which received significant media attention,2,15 finally enabled the BWWC to conduct their analysis and produce a report presenting their findings.4

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