Random-data perturbation techniques and privacy-preserving data mining

Privacy is becoming an increasingly important issue in many data-mining applications. This has triggered the development of many privacy-preserving data-mining techniques. A large fraction of them use randomized data-distortion techniques to mask the data for preserving the privacy of sensitive data. This methodology attempts to hide the sensitive data by randomly modifying the data values often using additive noise. This paper questions the utility of the random-value distortion technique in privacy preservation. The paper first notes that random matrices have predictable structures in the spectral domain and then it develops a random matrix-based spectral-filtering technique to retrieve original data from the dataset distorted by adding random values. The proposed method works by comparing the spectrum generated from the observed data with that of random matrices. This paper presents the theoretical foundation and extensive experimental results to demonstrate that, in many cases, random-data distortion preserves very little data privacy. The analytical framework presented in this paper also points out several possible avenues for the development of new privacy-preserving data-mining techniques. Examples include algorithms that explicitly guard against privacy breaches through linear transformations, exploiting multiplicative and colored noise for preserving privacy in data mining applications.