Protect

The proposed project will develop Visualization and Analysis of C Code Security (VACCS) tool to assist students with learning secure code programming. The proposal addresses the critical issue of learning secure coding through the development of a system for analyzing and visualizing C code and associated learning materials. VACCS will utilize static and dynamic program analysis to detect security vulnerabilities and warn programmers about the potential errors in their code.

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.

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.