In this paper, we describe our approach to achieve distributed differential privacy by sampling alone. Our mechanism works in the semihonest setting (honest-but-curious whereby aggregators attempt to peek at the data though follow the protocol). We show that the utility remains constant and does not degrade due to the variance as compared to the randomized response mechanism. In addition, we sh...

With demand for health services continuing to grow as populations age and new technologies emerge to meet health needs, healthcare policy-makers are under constant pressure to set priorities, ie, to make choices about the health services that can and cannot be funded within available resources. In a recent paper, Smith et al apply an influential policy studies framework – Kingdon’s multiple str...

Privacy regulations can affect the willingness to collect personal health data that may be disclosed to insurers. Perfect privacy cannot always be guaranteed. Consequently, people may refrain from collecting personal health data. This paper provides a theoretical and experimental analysis of the importance of privacy regulations for information acquisition and disclosure behavior. We contrast t...

Statistical Disclosure Control (SDC, for short) studies the problem of privacy-preserving data publishing in cases where the data is expected to be used for statistical analysis. An original dataset T containing sensitive information is transformed into a sanitized version T ′ which is released to the public. Both utility and privacy aspects are very important in this setting. For utility, T ′ ...

The purpose of this research, is to study factors influencing privacy concerns about data security and protection on social network sites and its’ influence on self-disclosure. 100 articles about privacy protection, data security, information disclosure and Information leakage on social networks were studied. Models and algorithms types and their repetition in articles have been distinguished a...

Cloud providers are realizing the outsourced database model in the form of database-as-a-service offerings. However, security in terms of data privacy remains an obstacle because data storage and processing are performed on an untrusted cloud. Achieving strong security under additional constraints of functionality and performance is even more challenging, for which advanced encryption and recen...

Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers proposed for utilization of interpersonal trust between users, to alleviate many of these crucial shortcomings. Till now, attention has been mainly paid to strong points about...

BACKGROUND Record linkage of existing individual health care data is an efficient way to answer important epidemiological research questions. Reuse of individual health-related data faces several problems: Either a unique personal identifier, like social security number, is not available or non-unique person identifiable information, like names, are privacy protected and cannot be accessed. A s...

We incorporate signaling scheme into Ad Auction setting, to achieve better welfare and revenue while protect users’ privacy. We propose a new K-anonymous signaling scheme setting, prove the hardness of the corresponding welfare/revenue maximization problem, and finally propose the algorithms to approximate the optimal revenue or welfare.

Social Network Sites (SNSs) such as Facebook offer a plethora of privacy controls, but users rarely exploit all of these controls, nor do they do so in a similar manner. In this paper, we analyze distinct profiles of users’ privacy management strategies on Facebook (including but also going beyond information disclosure behavior). We cluster the self-reported privacy behaviors of 308 Facebook u...