Owners of data may wish to share some statistics with others, but they may be worried of privacy of the underlying data. An effective solution to this problem is to employ provable privacy techniques, such as differential privacy, to add noise to the statistics before releasing them. This protection lowers the risk of sharing sensitive data with more or less trusted data sharing partners. Unfortunately, applying differential privacy in its mathematical form requires one to fix certain numeric parameters, which involves subtle computations and expert knowledge that the data owners may lack.In this paper, we first describe a differential privacy parameter selection procedure that minimizes what lay data owners need to know. Second, we describe a user visualization and workflow that makes this procedure available for lay data owners by helping them set the level of noise appropriately to achieve a tolerable risk level. Finally, we describe a user study in which human factors professionals who were native to differential privacy were briefly trained on the concept of using differential privacy for data sharing and then used the visualization to determine an appropriate level of noise.

We present Probabilistic Doxastic Temporal (PDT) Logic, a formalism to represent and reason about probabilistic beliefs and their temporal evolution in multi-agent systems. This formalism enables the quantification of agents’ beliefs through probability intervals and incorporates an explicit notion of time. We discuss how over time agents dynamically change their beliefs in facts, temporal rules, and other agents’ beliefs with respect to any new information they receive. We introduce an appropriate formal semantics for PDT Logic and show that it is decidable. Alternative options of specifying problems in PDT Logic are possible. For these problem specifications, we develop different satisfiability checking algorithms and provide complexity results for the respective decision problems. The use of probability intervals enables a formal representation of probabilistic knowledge without enforcing (possibly incorrect) exact probability values. By incorporating an explicit notion of time, PDT Logic provides enriched possibilities to represent and reason about temporal relations.