Toward efficient and privacy-preserving computing in big data era

Rongxing Lu, Hui Zhu, Ximeng Liu, Joseph K Liu, Jun Shao

Research output: Contribution to journalArticleResearchpeer-review

216 Citations (Scopus)

Abstract

Big data, because it can mine new knowledge for economic growth and technical innovation, has recently received considerable attention, and many research efforts have been directed to big data processing due to its high volume, velocity, and variety (referred to as “3V”) challenges. However, in addition to the 3V challenges, the flourishing of big data also hinges on fully understanding and managing newly arising security and privacy challenges. If data are not authentic, new mined knowledge will be unconvincing; while if privacy is not well addressed, people may be reluctant to share their data. Because security has been investigated as a new dimension, “veracity,” in big data, in this article, we aim to exploit new challenges of big data in terms of privacy, and devote our attention toward efficient and privacy-preserving computing in the big data era. Specifically, we first formalize the general architecture of big data analytics, identify the corresponding privacy requirements, and introduce an efficient and privacy-preserving cosine similarity computing protocol as an example in response to data mining’s efficiency and privacy requirements in the big data era.
Original languageEnglish
Pages (from-to)46-50
Number of pages5
JournalIEEE Network
Volume28
Issue number4
DOIs
Publication statusPublished - 2014
Externally publishedYes

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