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Title: Interval privacy: A new framework for privacy-preserving data collection Authors:  Jie Ding - University of Minnesota (United States) [presenting]
Abstract: The emerging public awareness and government regulations of data privacy motivate new paradigms of collecting and analyzing data transparent and acceptable to data owners. A new concept of privacy and related data formats, mechanisms, and theories for statistically privatizing data during data collection are introduced. The new privacy mechanisms will record each data value as a random interval (or, more generally, a range) containing it. Such mechanisms can be easily deployed through survey-based data collection interfaces, e.g., by asking a respondent whether their data value is within a randomly generated range. Using narrowed range to convey information is complementary to the popular paradigm of perturbing data. Also, the proposed mechanisms can generate progressively refined information at the discretion of individuals, naturally leading to privacy-adaptive data collection. Unique perspectives will be demonstrated, which are brought by Interval Privacy for human-centric data privacy, where individuals enjoy a perceptible, transparent, and simple way of sharing sensitive data.