Interval Privacy: A Framework for Data Collection

The emerging public awareness and government regulations of data privacy motivate new paradigms of collecting and analyzing data transparent and acceptable to data owners. We present a new concept of privacy and corresponding data formats, mechanisms, and tradeoffs for privatizing data during data collection. The privacy, named Interval Privacy, enforces the raw data conditional distribution on the privatized data to be the same as its unconditional distribution over a nontrivial support set. Correspondingly, the proposed privacy mechanism will record each data value as a random interval containing it. The proposed interval privacy mechanisms can be easily deployed through most existing survey-based data collection paradigms, e.g., by asking a respondent whether its data value is within a randomly generated range. Another unique feature of interval mechanisms is that they obfuscate the truth but not distort it. The way of using narrowed range to convey information is complementary to the popular paradigm of perturbing data. Also, the interval mechanisms can generate progressively refined information at the discretion of individual respondents. We study different theoretical aspects of the proposed privacy. In the context of supervised learning, we also offer a method such that existing supervised learning algorithms designed for point-valued data could be directly applied to learning from interval-valued data.