Many existing privacy-preserving techniques for querying distributed databases of sensitive information do not scale for large databases due to the use of heavyweight cryptographi...
Sherman S. M. Chow, Jie-Han Lee, Lakshminarayanan ...
It is often highly valuable for organizations to have their data analyzed by external agents. However, any program that computes on potentially sensitive data risks leaking inform...
— In the emerging cloud computing paradigm, data owners become increasingly motivated to outsource their complex data management systems from local sites to the commercial public...
Ning Cao, Zhenyu Yang, Cong Wang, Kui Ren, Wenjing...
In this paper we explore private computation built on vector addition and its applications in privacypreserving data mining. Vector addition is a surprisingly general tool for imp...
In this paper we introduce a framework for privacypreserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy ...
Yitao Duan, NetEase Youdao, John Canny, Justin Z. ...