Event monitoring and detection in real-time systems is crucial. Protecting users' data while reporting an event in almost real-time will increase the level of this challenge. In this work, we adopt the strong notion of differential privacy to private stream counting for event detection with the aim of minimizing false positive and false negative rates as our utility metrics.

Differential privacy is considered a de facto standard for private data analysis. However, the definition and much of the supporting literature applies to flat tables. While there exist variants of the definition and specialized algorithms for specific types of relational data (e.g. graphs), there isn't a general privacy definition for multi-relational schemas with constraints, and no system that permits accurate differentially private answering of SQL queries while imposing a fixed privacy budget across all queries posed by the analyst. This work presents PrivateSQL, a first-of-its-kind end-to-end differentially private relational database system. PrivateSQL allows an analyst to query data stored in a standard database management system using a rich class of SQL counting queries. PrivateSQL adopts a novel generalization of differential privacy to multi-relational data that takes into account constraints in the schema like foreign keys, and allows the data owner to flexibly specify entities in the schema that need privacy. PrivateSQL ensures a fixed privacy loss across all the queries posed by the analyst by answering queries on private synopses generated from several views over the base relation that are tuned to have low error on a representative query workload. We experimentally evaluate PrivateSQL on a real-world dataset and a workload of more than 3, 600 queries. We show that for 50% of the queries PrivateSQL offers at least 1, 000x better error rates than solutions adapted from prior work.

Hay et al. (2016) recently observed that existing histogram release mechanisms under differential privacy do not provide satisfactory privacy protection. Existing work either tunes on sensitive data to optimise parameters without consideration of privacy; or selection is performed arbitrarily and independent of data, degrading utility. We address this open problem by deriving a principled tuning mechanism E2EPRIV that privately optimises data-dependent error bounds. Theoretical analysis establishes privacy and utility, while extensive experimentation demonstrates that E2EPRIV can practically achieve true end-to-end privacy.

Mining of spatial data is an enabling technology for mobile services, Internet-connected cars, and the Internet of Things. But the very distinctiveness of spatial data that drives utility, comes at the cost of user privacy. In this work, we continue the tradition of privacy-preserving spatial analytics, focusing not on point or path data, but on planar spatial regions. Such data represents the area of a user's most frequent visitation-such as "around home and nearby shops". Specifically we consider the differentially-private release of data structures that support range queries for counting users' spatial regions. Counting planar regions leads to unique challenges not faced in existing work. A user's spatial region that straddles multiple data structure cells can lead to duplicate counting at query time. We provably avoid this pitfall by leveraging the Euler characteristic. To address the increased sensitivity of range queries to spatial region data, we calibrate privacy-preserving noise using bounded user region size and a constrained inference that uses robust least absolute deviations. Our novel constrained inference reduces noise and introduces covertness by (privately) imposing consistency. We provide a full end-to-end theoretical analysis of both differential privacy and high-probability utility for our approach using concentration bounds. A comprehensive experimental study on several real-world datasets establishes practical validity.

Call centers, or their contemporary successors, contact centers, are the preferred and prevalent way for many companies to communicate with their customers. The call center industry is thus vast and rapidly expanding in terms of both workforce and economic scope. It enjoys an annual growth rate of 20% and, overall, more than half of the business transactions are conducted over the phone. The design of such a system, and the management of its performance, surely must be based on sound scientific principles. Our goal in this paper is to survey the structure and technology of call center systems, point out some available special modeling and analysis tools, propose a high-level generalized stochastic Petri net (GSPN) model, and evaluate the model, aiming to minimize the workload of agents (servers) in order to provide self-serve.