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Data mining can extract important knowledge from large data collections – but sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data, and some types of information about the data. This paper addresses secure mining of association rules over horizontally partitioned data. The(More)
Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be(More)
The advent of cloud computing has ushered in an era of mass data storage in remote servers. Remote data storage offers reduced data management overhead for data owners in a cost effective manner. Sensitive documents, however, need to be stored in encrypted format due to security concerns. But, encrypted storage makes it difficult to search on the stored(More)
Integrating data from multiple sources has been a longstanding challenge in the database community. Techniques such as privacy-preserving data mining promises privacy, but assume data has integration has been accomplished. Data integration methods are seriously hampered by inability to share the data to be integrated. This paper lays out a privacy framework(More)
For over fifty years, " record linkage " procedures have been refined to integrate data in the face of typographical and semantic errors. These procedures are traditionally performed over personal iden-tifiers (e.g., names), but in modern decentralized environments, privacy concerns have led to regulations that require the obfuscation of such attributes.(More)
OBJECTIVE Integration of patients' records across resources enhances analytics. To address privacy concerns, emerging strategies such as Bloom filter encodings (BFEs), enable integration while obscuring identifiers. However, recent investigations demonstrate BFEs are, in theory, vulnerable to cryptanalysis when encoded identifiers are randomly selected from(More)
The existence of on-line social networks that include person specific information creates interesting opportunities for various applications ranging from marketing to community organization. On the other hand, security and privacy concerns need to be addressed for creating such applications. Improving social network access control systems appears as the(More)
On-line social networks, such as Facebook, are increasingly utilized by many users. These networks allow people to publish details about themselves and connect to their friends. Some of the information revealed inside these networks is private and it is possible that corporations could use learning algorithms on the released data to predict undisclosed(More)
Privacy and security concerns can prevent sharing of data, derailing data-mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. We introduce a generalized privacy-preserving variant of the ID3 algorithm for vertically partitioned data distributed over two or more parties. Along with a proof of security, we discuss(More)