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Title: Data Confidentiality Challenges in Big Data Applications

In this paper, we address the problem of data confidentiality in big data analytics. In many fields, much useful patterns can be extracted by applying machine learning techniques to big data. However, data confidentiality must be protected. In many scenarios, data confidentiality could well be a prerequisite for data to be shared. We present a scheme to provide provable secure data confidentiality and discuss various techniques to optimize performance of such a system.
Authors:
;
Publication Date:
OSTI Identifier:
1267598
Report Number(s):
PNNL-SA-114675
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Big Data (Big Data), October 29-November 1, 2015, Santa Clara, California, 2886-2888
Publisher:
IEEE, Piscataway, New Jersey
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
data confidentiality