An Evolutionary Optimization Approach for Categorical Data Protection
Publication Type:Conference Paper
Source:Privacy and Anonymity in the Information Society 2012, Berlin (2012)
Keywords:genetic algorithms; data privacy; categorical data; data mining; information loss; disclosure risk
The continuous growing amount of public sensible data has increased the risk of breaking the privacy of people or institutions in those datasets. Many protection methods have been developed to solve this problem by either distorting or generalizing data but taking into account the difficult trade-off between data utility (information loss) and protection against disclosure (disclosure risk).
In this paper we present an optimization approach for data protection based on an evolutionary algorithm which is guided by a combination of information loss and disclosure risk measures. In this way, state-of-the-art protection methods are combined to obtain new data protections with a better trade-off between these two measures. The paper presents several experimental results that assess the performance of our approach.
Clustering-based Information Loss for Data Protection Methods of Categorical Data
Source:Universitat Autònoma de Barcelona, Bellaterra (Barcelona), Spain, p.24 (2011)
Keywords:Data Privacy; Information Loss; Disclosure Risk; Clustering
Data privacy has been always a very important issue but it became much more important with the expansion of the Internet because, nowadays, the number of public datasets avaliable for statistical studies is growing more and more, so the amount of sensitive data available on the Internet is greater every day. This fact makes very important the assessment of the performance of all the methods used to mask those datasets. In order to check the performance there exist two kind of measures: the information loss and the disclosure risk. This performance assessment comes even more important when protecting categorical data which has a very limited manipulation.
In this thesis I present an information loss analysis based on cluster-specific measures over categorical data protection methods. That is, measures specifically defined for the case in which the user will do clustering with the data. We also compare the obtained results with the ones known using general information loss analysis.
A Comparison of Two Different Types of Online Social Network from a Data Privacy Perspective
Publication Type:Book Chapter
Source:Lecture Notes in Artificial Intelligence, Springer, Volume 6820, p.223-234 (2011)
Keywords:Social network; data privacy; descriptive statistics; risk of disclosure; information loss
We consider two distinct types of online social network, the first made up of a log of writes to wall by users in Facebook, and the second consisting of a corpus of emails sent and received in a corporate environment (Enron). We calculate the statistics which describe the topologies of each network represented as a graph. Then we calculate the information loss and risk of disclosure for different percentages of perturbation for each dataset, where perturbation is achieved by randomly adding links to the nodes. We find that the general tendency of information loss is similar, although Facebook is affected to a greater extent. For risk of disclosure, both datasets also follow a similar trend, except for the average path length statistic. We find that the differences are due to the different distributions of the derived factors, and also the type of perturbation used and its parameterization. These results can be useful for choosing and tuning anonymization methods for different graph datasets.
Evaluation of Information Loss for Privacy Preserving Data Mining through comparison of Fuzzy Partitions
PRAM Optimization Using an Evolutionary Algorithm
Publication Type:Book Chapter
Source:Privacy in Statistical Databases, Springer, Number LNCS 6344, Corfú, Greece, p.97 - 106 (2010)
Keywords:Information Privacy and Security; Evolutionary Algorithms; Post Randomization Method; Information Loss; Disclosure Risk
PRAM (Post Randomization Method) was introduced in 1997 but it is still one of the least used methods in statistical categorical data protection. This fact is because of the difficulty to obtain a good transition matrix in order to obtain a good protection. In this paper, we describe how to obtain a better protection using an evolutionary algorithm with integrated information loss and disclosure risk measures to find the best matrix. We also provide experiments using a real dataset of 1000 records in order to empirically evaluate the application of this technique.