Privacy-Preserving Trust Management Mechanisms from Private Matching Schemes

Cryptographic primitives are essential for constructing privacy-preserving communication mechanisms. There are situations in which two parties that do not know each other need to exchange sensitive information on the Internet. Trust management mechanisms make use of digital credentials and certificates in order to establish trust among these strangers. We address the problem of choosing which credentials are exchanged. During this process, each party should learn no information about the preferences of the other party other than strictly required for trust establishment. We present a method to reach an agreement on the credentials to be exchanged that preserves the privacy of the parties. Our method is based on secure two-party computation protocols for set intersection. Namely, it is constructed from private matching schemes.Comment: The material in this paper will be presented in part at the 8th DPM International Workshop on Data Privacy Management (DPM 2013

Dwarna : a blockchain solution for dynamic consent in biobanking

Dynamic consent aims to empower research partners and facilitate active participation in the research process. Used within the context of biobanking, it gives individuals access to information and control to determine how and where their biospecimens and data should be used. We present Dwarna—a web portal for ‘dynamic consent’ that acts as a hub connecting the different stakeholders of the Malta Biobank: biobank managers, researchers, research partners, and the general public. The portal stores research partners’ consent in a blockchain to create an immutable audit trail of research partners’ consent changes. Dwarna’s structure also presents a solution to the European Union’s General Data Protection Regulation’s right to erasure—a right that is seemingly incompatible with the blockchain model. Dwarna’s transparent structure increases trustworthiness in the biobanking process by giving research partners more control over which research studies they participate in, by facilitating the withdrawal of consent and by making it possible to request that the biospecimen and associated data are destroyed.peer-reviewe

Watching You: Systematic Federal Surveillance of Ordinary Americans

To combat terrorism, Attorney General John Ashcroft has asked Congress to "enhance" the government's ability to conduct domestic surveillance of citizens. The Justice Department's legislative proposals would give federal law enforcement agents new access to personal information contained in business and school records. Before acting on those legislative proposals, lawmakers should pause to consider the extent to which the lives of ordinary Americans already are monitored by the federal government. Over the years, the federal government has instituted a variety of data collection programs that compel the production, retention, and dissemination of personal information about every American citizen. Linked through an individual's Social Security number, these labor, medical, education and financial databases now empower the federal government to obtain a detailed portrait of any person: the checks he writes, the types of causes he supports, and what he says "privately" to his doctor. Despite widespread public concern about preserving privacy, these data collection systems have been enacted in the name of "reducing fraud" and "promoting efficiency" in various government programs. Having exposed most areas of American life to ongoing government scrutiny and recording, Congress is now poised to expand and universalize federal tracking of citizen life. The inevitable consequence of such constant surveillance, however, is metastasizing government control over society. If that happens, our government will have perverted its most fundamental mission and destroyed the privacy and liberty that it was supposed to protect

Economic location-based services, privacy and the relationship to identity

Mobile telephony and mobile internet are driving a new application paradigm: location-based services (LBS). Based on a person’s location and context, personalized applications can be deployed. Thus, internet-based systems will continuously collect and process the location in relationship to a personal context of an identified customer. One of the challenges in designing LBS infrastructures is the concurrent design for economic infrastructures and the preservation of privacy of the subjects whose location is tracked. This presentation will explain typical LBS scenarios, the resulting new privacy challenges and user requirements and raises economic questions about privacy-design. The topics will be connected to “mobile identity” to derive what particular identity management issues can be found in LBS

Fighting Terrorism in an Electronic Age: Does the Patriot Act Unduly Compromise Our Civil Liberties?

The USA PATRIOT Act is tremendously controversial, both lauded by law enforcement and decried by civil liberties groups. This iBrief considers two of the Act\u27s communications monitoring provisions, concluding that each compromises civil liberties to a greater degree than is necessary to combat terrorism. Accordingly, Congress should revise the USA PATRIOT Act, bringing it into line with the Constitution

Supporting Regularized Logistic Regression Privately and Efficiently

As one of the most popular statistical and machine learning models, logistic regression with regularization has found wide adoption in biomedicine, social sciences, information technology, and so on. These domains often involve data of human subjects that are contingent upon strict privacy regulations. Increasing concerns over data privacy make it more and more difficult to coordinate and conduct large-scale collaborative studies, which typically rely on cross-institution data sharing and joint analysis. Our work here focuses on safeguarding regularized logistic regression, a widely-used machine learning model in various disciplines while at the same time has not been investigated from a data security and privacy perspective. We consider a common use scenario of multi-institution collaborative studies, such as in the form of research consortia or networks as widely seen in genetics, epidemiology, social sciences, etc. To make our privacy-enhancing solution practical, we demonstrate a non-conventional and computationally efficient method leveraging distributing computing and strong cryptography to provide comprehensive protection over individual-level and summary data. Extensive empirical evaluation on several studies validated the privacy guarantees, efficiency and scalability of our proposal. We also discuss the practical implications of our solution for large-scale studies and applications from various disciplines, including genetic and biomedical studies, smart grid, network analysis, etc