Schedules define how resources process jobs in diverse domains, reaching from healthcare to transportation, and, therefore, denote a valuable starting point for analysis of the underlying system. However, publishing schedule may disclose private information on considered jobs. In this paper, we provide first threat model published schedules, thereby defining completely new class data privacy pr...

Differential privacy (DP) has become the de facto standard of preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph process, machine learning, deep federated learning. Although DP an active influential area, it not best remedy for all problems scenarios. Moreover, there are also some misund...

background: this study aimed to evaluate the design of emergency departâ­ments regarding to the patients’ and staff's privacy, confidentiality and facilities in genâ­eral teaching hospitals. methods: emergency departments of all the general teaching hospitals of shahid beâ­heshti university of medical sciences, tehran, iran were evaluated in 2007 through both direct observation and interview ...

This article first describes the privacy concerns that arise due to data mining, especially for national security applications. Then we discuss privacy-preserving data mining. In particular, we view the privacy problem as a form of inference problem and introduce the notion of privacy constraints. We also describe an approach for privacy constraint processing and discuss its relationship to pri...

Wireless sensor network (WSN) technology has the potential to change the way we live, work, protect and do business, with applications in entertainment, travel, industry, telemedicine, disaster and emergency management. Data aggregation is key technique for power-efficient information acquisition in WSNs. However, data privacy during data aggregation is an important issue when the WSN is deploy...