Analysis of privacy preserving random perturbation techniques

2003 ◽  
Author(s):  
Haimonti Dutta ◽  
Hillol Kargupta ◽  
Souptik Datta ◽  
Krishnamoorthy Sivakumar
Keyword(s):  
Random Perturbation ◽  
Privacy Preserving ◽  
Perturbation Techniques

Robust Analysis on a Privacy Preserving Recommendation Algorithm under the KNN Attack

2014 ◽  
Vol 610 ◽  
pp. 717-721 ◽  
Author(s):  
Yan Gao ◽  
Jing Bo Xia ◽  
Jing Jing Ji ◽  
Ling Ma
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Privacy Preserving ◽  
Perturbation Techniques ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Robust Analysis ◽  
History Of

— Among algorithms in recommendation system, Collaborative Filtering (CF) is a popular one. However, the CF methods can’t guarantee the safety of the user rating data which cause private preserving issue. In general, there are four kinds of methods to solve private preserving: Perturbation, randomization, swapping and encryption. In this paper, we mimic algorithms which attack the privacy-preserving methods with randomized perturbation techniques. After leaking part of rating history of a customer, we can infer this customer’s other rating history. At the end, we propose an algorithm to enhance the system so as to avoid being attacked.

Random-data perturbation techniques and privacy-preserving data mining

2005 ◽  
Vol 7 (4) ◽  
pp. 387-414 ◽  
Author(s):  
Hillol Kargupta ◽  
Souptik Datta ◽  
Qi Wang ◽  
Krishnamoorthy Sivakumar
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Perturbation Techniques ◽  
Data Perturbation ◽  
Random Data ◽  
Privacy Preserving Data Mining
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