DocumentCode :
659479
Title :
Zero-knowledge private graph summarization
Author :
Shoaran, Mahsa ; Thomo, Alex ; Weber-Jahnke, Jens H.
Author_Institution :
Univ. of Victoria, Victoria, BC, Canada
fYear :
2013
fDate :
6-9 Oct. 2013
Firstpage :
597
Lastpage :
605
Abstract :
Graphs have become increasingly popular for modeling data in a wide variety of applications, and graph summarization is a useful technique to analyze information from large graphs. Privacy preserving mechanisms are vital to protect the privacy of individuals or institutions when releasing aggregate numbers, such as those in graph summarization. We propose privacy-aware release of graph summarization using zero-knowledge privacy (ZKP), a recently proposed privacy framework that is more effective than differential privacy (DP) for graph and social network databases. We first define group-based graph summaries. Next, we present techniques to compute the parameters required to design ZKP methods for each type of aggregate data. Then, we present an approach to achieve ZKP for probabilistic graphs.
Keywords :
data analysis; data privacy; graph theory; mathematics computing; DP; ZKP framework; aggregate data; data modeling; differential privacy; graph databases; group-based graph summaries; information analysis; privacy preserving mechanisms; probabilistic graphs; social network databases; zero-knowledge private graph summarization; Aggregates; Complexity theory; Data privacy; Databases; Noise; Privacy; Social network services;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data, 2013 IEEE International Conference on
Conference_Location :
Silicon Valley, CA
Type :
conf
DOI :
10.1109/BigData.2013.6691628
Filename :
6691628
Link To Document :
بازگشت