Access the full text.
Sign up today, get DeepDyve free for 14 days.
WB Johnson, J Lindenstrauss (1984)
Extension of Lipshitz mappings into Hilbert spaceContemp Math, 26
JL Herlocker, JA Konstan, LG Terveen, JT Riedl (2004)
Evaluating collaborative filtering recommender systemsACM Trans Inform Syst, 22
GW Stewart, J Sun (1990)
Matrix perturbation theory
CR Giannella, K Liu, H Kargupta (2013)
Breaching Euclidean distance-preserving data perturbation using few known inputsData Knowl Eng, 83
E Kaplan, TB Pedersen, E Savas, Y Saygin (2010)
Discovering private trajectories using background informationData Knowl Eng, 69
S Guo, X Wu, Y Li (2008)
Determining error bounds for spectral filtering based reconstruction methods in privacy preserving data miningKnowl Inf Syst, 17
Y Saygin, VS Verykios, C Clifton (2001)
Using unknowns to prevent discovery of association rulesSIGMOD Rec, 30
SL Warner (1965)
Randomized response: a survey technique for eliminating evasive answer biasJ Am Stat Assoc, 60
K Liu, C Giannella, H Kargupta (2008)
Privacy-preserving data mining: models and algorithms
K Muralidhar, R Parsa, R Sarathy (1999)
A general additive data perturbation method for database securityManage Sci, 45
S Kullback, RA Leibler (1951)
On information and sufficiencyAnn Math Stat, 22
K Liu, H Kargupta, J Ryan (2006)
Random projection-based multiplicative data perturbation for privacy preserving distributed data miningIEEE Tran Knowl Data Eng, 18
GJ Székely, ML Rizzo (2004)
Testing for equal distributions in high dimensionInterStat, 5
H Kargupta, S Datta, Q Wang, K Sivakumar (2005)
Random-data perturbation techniques and privacy-preserving data miningKnowl Inf Syst, 7
S Mukherjee, S Banerjee, Z Chen, A Gangopadhyay (2008)
A privacy preserving technique for distance-based classification with worst case privacy guaranteesData Knowl Eng, 66
Y Sang, H Shen, H Tian (2012)
Effective reconstruction of data perturbed by random projectionsIEEE Trans Comput, 61
A Hyvärinen, J Karhunen, E Oja (2001)
Independent component analysis
L Guo, X Wu (2009)
Privacy preserving categorical data analysis with unknown distortion parametersTrans Data Priv, 2
K Kenthapadi, A Korolova, I Mironov, N Mishra (2013)
Privacy via the Johnson–Lindenstrauss transformJ Priv Confid, 5
J Zhao, J Yang, J Zhang (2014)
Privacy properties of random projection perturbation when random matrix is leakingJ Comput Inf Syst, 10
A Amiri (2007)
Dare to share: protecting sensitive knowledge with data sanitizationDecis Support Syst, 43
Privacy-preserving data mining has attracted the attention of a large number of researchers. Many data perturbation methods have been proposed to ensure individual privacy. Such methods seem to be successful in providing privacy and accuracy. On one hand, different methods are utilized to preserve privacy. On the other hand, various data reconstruction approaches have been proposed to derive private information from perturbed data. Thus, many researchers have been conducting various studies about data reconstruction methods and the resilience of data perturbation schemes. In this survey, we focus on data reconstruction methods due to their importance in privacy-preserving data mining. We provide a detailed review of the data reconstruction methods and the data perturbation schemes attacked by different data reconstruction techniques. We merge our review with the evaluation metrics and the data sets used in current attack techniques. Finally, we pose some open questions to provide a better understanding of these approaches and to guide future study.
Artificial Intelligence Review – Springer Journals
Published: Sep 14, 2015
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.