Interval Privacy: A Framework for Privacy-Preserving Data Collection

TL;DR

This paper introduces Interval Privacy, a novel framework for privacy-preserving data collection that records data as ranges, maintaining distributional properties while allowing transparent, human-centric privacy mechanisms.

Contribution

It proposes a new privacy concept with mechanisms that record data as intervals, enabling privacy-preserving, transparent, and adaptive data collection methods.

Findings

Interval privacy mechanisms preserve data distribution while obfuscating individual data.

The mechanisms are easily deployable via survey interfaces asking about data ranges.

Theoretical foundations include composition, robustness, and data analysis techniques for interval data.

Abstract

The emerging public awareness and government regulations of data privacy motivate new paradigms of collecting and analyzing data that are transparent and acceptable to data owners. We present a new concept of privacy and corresponding data formats, mechanisms, and theories 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 (or, more generally, a range) containing it. The proposed interval privacy mechanisms can be easily deployed through survey-based data collection interfaces, 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 do not perturb it. 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 individuals, naturally leading to privacy-adaptive data collection. We develop different aspects of theory such as composition, robustness, distribution estimation, and regression learning from interval-valued data. Interval privacy provides a new perspective of human-centric data privacy where individuals have a perceptible, transparent, and simple way of sharing sensitive data.