Protection of big data privacy

In recent years, big data have become a hot research topic. The increasing amount of big data also increases the chance of breaching the privacy of individuals. Since big data require high computational power and large storage, distributed systems are used. As multiple parties are involved in these systems, the risk of privacy violation is increased. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (e.g., data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a comprehensive overview of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. In particular, in this paper, we illustrate the infrastructure of big data and the state-of-the-art privacy-preserving mechanisms in each stage of the big data life cycle. Furthermore, we discuss the challenges and future research directions related to privacy preservation in big data

William H. Sorrell, Attorney General of Vermont, et al. v. IMS Health Inc., et al. - Amicus Brief in Support of Petitioners

On April 26, 2011, the US Supreme Court will hear oral arguments in the Vermont data mining case, Sorrell v. IMS Health Inc. Respondents claim this is the most important commercial speech case in a decade. Petitioner (the State of Vermont) argues this is the most important medical privacy case since Whalen v. Roe. The is an amicus brief supporting Vermont, written by law professors and submitted on behalf of the New England Journal of Medicin

An Analysis and Enumeration of the Blockchain and Future Implications

The blockchain is a relatively new technology that has grown in interest and potential research since its inception. Blockchain technology is dominated by cryptocurrency in terms of usage. Research conducted in the past few years, however, reveals blockchain has the potential to revolutionize several different industries. The blockchain consists of three major technologies: a peer-to-peer network, a distributed database, and asymmetrically encrypted transactions. The peer-to-peer network enables a decentralized, consensus-based network structure where various nodes contribute to the overall network performance. A distributed database adds additional security and immutability to the network. The process of cryptographically securing individual transactions forms a core service of the blockchain and enables semi-anonymous user network presence

Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social engineering techniques to infect their machines. Research showed that machine-learning algorithms provide effective detection mechanisms against such threats, but the existence of an arms race in adversarial settings has recently challenged such systems. In this work, we focus on malware embedded in PDF files as a representative case of such an arms race. We start by providing a comprehensive taxonomy of the different approaches used to generate PDF malware, and of the corresponding learning-based detection systems. We then categorize threats specifically targeted against learning-based PDF malware detectors, using a well-established framework in the field of adversarial machine learning. This framework allows us to categorize known vulnerabilities of learning-based PDF malware detectors and to identify novel attacks that may threaten such systems, along with the potential defense mechanisms that can mitigate the impact of such threats. We conclude the paper by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings