  Data sharing enables critical advances in many research areas and business
applications, but it may lead to inadvertent disclosure of sensitive summary
statistics (e.g., means or quantiles). Existing literature only focuses on
protecting a single confidential quantity, while in practice, data sharing
involves multiple sensitive statistics. We propose a novel framework to define,
analyze, and protect multi-secret summary statistics privacy in data sharing.
Specifically, we measure the privacy risk of any data release mechanism by the
worst-case probability of an attacker successfully inferring summary statistic
secrets. Given an attacker's objective spanning from inferring a subset to the
entirety of summary statistic secrets, we systematically design and analyze
tailored privacy metrics. Defining the distortion as the worst-case distance
between the original and released data distribution, we analyze the tradeoff
between privacy and distortion. Our contribution also includes designing and
analyzing data release mechanisms tailored for different data distributions and
secret types. Evaluations on real-world data demonstrate the effectiveness of
our mechanisms in practical applications.
