Generalized differential privacy: regions of priors that admit robust optimal mechanisms

International audienceDifferential privacy is a notion of privacy that was initially designed for statistical databases, and has been recently extended to a more general class of domains. Both differential privacy and its generalized version can be achieved by adding random noise to the reported data. Thus, privacy is obtained at the cost of reducing the data's accuracy, and therefore their utility. In this paper we consider the problem of identifying optimal mechanisms for gen- eralized differential privacy, i.e. mechanisms that maximize the utility for a given level of privacy. The utility usually depends on a prior distribution of the data, and naturally it would be desirable to design mechanisms that are universally optimal, i.e., optimal for all priors. However it is already known that such mechanisms do not exist in general. We then characterize maximal classes of priors for which a mechanism which is optimal for all the priors of the class does exist. We show that such classes can be defined as convex polytopes in the priors space. As an application, we consider the problem of privacy that arises when using, for instance, location-based services, and we show how to define mechanisms that maximize the quality of service while preserving the desired level of geo- indistinguishability

A Mixed-Methods Investigation of Factors and Scenarios Influencing College Students’ Decision to Complete Surveys at Five Mid-Western Universities

Achieving respectable response rates to surveys on university campuses has become increasingly more difficult, which can increase non-response error and jeopardize the integrity of data. Prior research has focused on investigating the effect of a single or small set of factors on college students’ decision to complete surveys. We used a concurrent mixed-method design to examine (1) college students’ rationales for choosing to complete or not complete a survey presented to them and (2) their perceptions on the importance of multiple factors on their decision to complete or not complete surveys in a higher education setting. A total of 837 undergraduate and graduate students across five institutions in the state of Ohio completed the qualitative survey component, 808 completed the 72-scenario close-ended survey component, and 701 completed the rank- order component. The survey was administered in the classroom either at the beginning or end of the class period. The college students reported that the person administering, topic, incentives, length, and method of administration are the factors most influencing their decision to complete a survey. The undergraduate students were significantly more likely than graduate students to include incentives as one of the top three important factors in deciding to complete a survey. Qualitative results additionally revealed that the students felt day/time and location of survey request plays an important role in their decision. Recommendations are provided to survey administrators regarding potential effective and ineffective survey recruitment strategies

Maximizing the Conditional Expected Reward for Reaching the Goal

The paper addresses the problem of computing maximal conditional expected accumulated rewards until reaching a target state (briefly called maximal conditional expectations) in finite-state Markov decision processes where the condition is given as a reachability constraint. Conditional expectations of this type can, e.g., stand for the maximal expected termination time of probabilistic programs with non-determinism, under the condition that the program eventually terminates, or for the worst-case expected penalty to be paid, assuming that at least three deadlines are missed. The main results of the paper are (i) a polynomial-time algorithm to check the finiteness of maximal conditional expectations, (ii) PSPACE-completeness for the threshold problem in acyclic Markov decision processes where the task is to check whether the maximal conditional expectation exceeds a given threshold, (iii) a pseudo-polynomial-time algorithm for the threshold problem in the general (cyclic) case, and (iv) an exponential-time algorithm for computing the maximal conditional expectation and an optimal scheduler.Comment: 103 pages, extended version with appendices of a paper accepted at TACAS 201