_:n4716ac8d4d6b4b12a85d46a9dafa6c1dxb0
<http://schema.org/description>
"Differential privacy is a mathematical concept that provides an\ninformation-theoretic security guarantee. While differential privacy has\nemerged as a de facto standard for guaranteeing privacy in data sharing, the\nknown mechanisms to achieve it come with some serious limitations. Utility\nguarantees are usually provided only for a fixed, a priori specified set of\nqueries. Moreover, there are no utility guarantees for more complex - but very\ncommon - machine learning tasks such as clustering or classification. In this\npaper we overcome some of these limitations. Working with metric privacy, a\npowerful generalization of differential privacy, we develop a polynomial-time\nalgorithm that creates a private measure from a data set. This private measure\nallows us to efficiently construct private synthetic data that are accurate for\na wide range of statistical analysis tools. Moreover, we prove an\nasymptotically sharp min-max result for private measures and synthetic data for\ngeneral compact metric spaces. A key ingredient in our construction is a new\nsuperregular random walk, whose joint distribution of steps is as regular as\nthat of independent random variables, yet which deviates from the origin\nlogarithmicaly slowly."
<https://deepai.org/publication/private-measures-random-walks-and-synthetic-data>