DocumentCode :
3266223
Title :
Privacy-Preserving Statistical Analysis Method for Real-World Data
Author :
Ishii, Jun ; Maeomichi, Hiroyuki ; Yoda, Ikuo
Author_Institution :
NTT Network Innovation Labs., NTT Corp., Tokyo, Japan
fYear :
2012
fDate :
25-27 June 2012
Firstpage :
807
Lastpage :
812
Abstract :
We propose a method for obtaining statistical results such as averages, variances, and correlations without leaking any raw data values from data-holders by using multiple pseudonyms. At present, to obtain statistical results using a large amount of data, we need to collect all data in the same storage device. However, gathering real-world data that was generated by different people is not easy because they often contain private information. Thus, our method solves the problem and protects data-holders from data-user´s malicious attacks. Finally, we evaluate the suitability of our method through implementation and experimentation.
Keywords :
data privacy; statistical analysis; average; correlations; data-holder protection; data-user malicious attacks; privacy-preserving statistical analysis method; pseudonyms; real-world data; variances; Correlation; Data privacy; Privacy; Reliability; Servers; Smart phones; multiple pseudonyms; privacy-preserving; query auditing; splitting role of servers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Trust, Security and Privacy in Computing and Communications (TrustCom), 2012 IEEE 11th International Conference on
Conference_Location :
Liverpool
Print_ISBN :
978-1-4673-2172-3
Type :
conf
DOI :
10.1109/TrustCom.2012.226
Filename :
6296052
Link To Document :
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