In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated

A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models

, if any, perceivable overhead when running thirdparty applications. We use TaintDroid to study the behavior of 30 popular third-party Android applications and find several instances of misuse of users ’ private information. We believe that TaintDroid is the first working prototype demonstrating

-anonymity protection if the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. This paper also examines re-identification attacks that can be realized on releases that adhere to kanonymity unless accompanying

Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called k-anonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k − 1 other records

We describe schemes that enable a user to access k replicated copies of a database ( k * 2) and privately retrieve informationstored in the database. This means that each individual database gets no information on the identity of the item retrieved by the user. For a single database, achieving

Abstract. Since privacy information can be inferred via social relations, the privacy confidentiality problem becomes increasingly challenging as online social network services are more popular. Using a Bayesian network approach to model the causal relations among people in social networks, we

Abstract A promising technique for protecting privacy and integrity of sensitive data is to statically check information flow within programs that manipulate the data. While previous work has proposed programming language extensions to allow this static checking, the resulting languages are too

ABSTRACT This paper proposes a detailed framework of storing privacy information in surveillance video as a watermark. Authorized personnel is not only removed from the surveillance video as in [1] but also embedded into the video itself, which can only be retrieved with a secrete key. A

With the rapid growth of information and communication technology (ICT), the violation of information privacy has increased in recent years. The privacy concerns now re-emerge right because people perceives a threat from new ICT that are equipped with enhanced capabilities for surveillance, storage