  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.
