Scientific Foundations

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.

Outsourcing storage to the cloud has become more widespread in recent years; however, cloud storage services are constantly exposed to a number of non-trivial adversarial threats. This work addresses security risks arising from the leakage of access patterns, which is the ability of an adversary to detect when the same item is accessed repeatedly on a storage server, which has been shown to substantially impact data privacy.

Spurred by developments such as cloud computing, there has been considerable interest in the data-mining-as-a-service (DMaS) paradigm in which a client outsources his/her data mining needs to a third-party service provider. However, this raises a few security concerns. One of the security concerns is that the service provider may return plausible but incorrect mining results to the client.

Modifications to integrated circuits (ICs) or the insertion of foreign intellectual property pose a serious threat to U.S. sovereignty, as ICs are found in many consumer electronic devices, including phones, computers, and televisions. More importantly, many commercial and military U.S. assets rely on ICs for computation and management of critical infrastructure such as banking, energy, and defense systems. The primary impact of the proposed work is to improve the security of U.S.

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.