A practical scenario of PPML is where only one central party has the entire data on which the ML algorithm has to be learned. Agrawal and Ramakrishnan [1] proposed the first method to learn a Decision Tree classifier on a database without revealing any information about individual records. They consider public model private data setting where the algorithm and its parameters are public whereas ...

The right to privacy is not absolute and is often established by context and the need to know. The nature of the university environment sometimes distorts the sanctity of privacy because the "need to know" is so profuse. Although students are guaranteed the right to keep essential but confidential information private under the Family Educational Rights and Privacy Act of 1974, student data are ...

When requesting a web-based service, users often fail in setting the website’s privacy settings according to their self preferences. Being overwhelmed by choice of preferences, lack knowledge related technologies or unawareness own preferences are just some reasons why tend struggle. To address all these problems, prediction tools particularly well-suited. Such aim lower burden set owners’ be l...

The iterative nature of the expectation maximization (EM) algorithm presents a challenge for privacy-preserving estimation, as each iteration increases the amount of noise needed. We propose a practical private EM algorithm that overcomes this challenge using two innovations: (1) a novel moment perturbation formulation for differentially private EM (DP-EM), and (2) the use of two recently devel...

Recently, there has been a number of papers relating mechanism design and privacy (e.g., see [MT07, Xia11, CCK11, NST12, NOS12, HK12]). All of these papers consider a worst-case setting where there is no probabilistic information about the players’ types. In this paper, we investigate mechanism design and privacy in the Bayesian setting, where the players’ types are drawn from some common distr...