Deter

Social networks provide many benefits, but also give rise to serious concerns regarding privacy. Indeed, since privacy protections are not intrinsically incorporated into the underlying technological framework, user data is still accessible to the social network and is open to misuse. While there have been efforts to incorporate privacy into social networks, existing solutions are not sufficiently lightweight, transparent, and functional, and therefore have achieved only limited adoption.

Big data analytics can revolutionize innovation and productivity across diverse domains. However, this requires sharing or joint analysis of data, which is often inhibited due to privacy and security concerns. While techniques have been developed to enable the safe use of data for analysis, none of these work for the critical task of outlier detection. Outlier detection is one of the most fundamental data analysis tasks, useful in applications as far ranging as homeland security, to medical informatics, to financial fraud.

As companies collect consumer data in increasingly larger quantity and mine the data more deeply, trade-offs arise with respect to companies' practices about information privacy. A company may choose practices that augment targeted advertisements or services. However, the financial rewards associated with privacy practices are highly uncertain, since they are affected by a company's competition with rivals.

The adoption of smartphones has steadily increased in the past few years, and smartphones have become the tool with which millions of users handle confidential information, such as financial and health-related data. As a result, these devices have become attractive targets for cybercriminals, who attempt to violate the trust assumptions underlying the smartphone platform in order to compromise the security and privacy of users.

Many organizations and individuals rely on the cloud to store their data and process their analytical queries. But such data may contain sensitive information. Not only do users want to conceal their data on a cloud, they may also want to hide analytical queries over their data, results of such queries, and data access patterns from a cloud service provider (that may be compromised either from within or by a third party).