RoD: Evaluating the Risk of Data Disclosure Using Noise Estimation for Differential Privacy
Yao-Tung Tsou, Hung-Li Chen, Jia-Yang Chen · IEEE Transactions on Big Data · 2019
Differential privacy is a paradigm of big data privacy protection that offers protection even when an attacker has arbitrary background knowledge in advance. Consequently, it is viewed as a reliable protection mechanism for sensitive information. Differential privacy introduces noise, such as Laplace noise, to obfuscate the true values in a data set while preserving its statistical properties. However, a large amount of Laplace noise added into a data set is typically defined by the discursive scale parameter of Laplace distribution. The privacy budget$\varepsilon$in differential privacy has been theoretically interpreted, but the implication on the risk of data disclosure (RoD) in practice has not yet been well studied. Moreover, choosing an appropriate value for$\varepsilon$is not straightforward because it considerably affects the level of privacy in a data set. In this paper, we define and evaluateRoDin a data set with either numerical or binary attributes for numerical or counting queries with multiple attributes based on noise estimation. Through confidence probability of noise estimation, we provide a simple method to select the privacy budget$\varepsilon$for differential privacy and associate differential privacy with$k$-anonymization. Finally, we show the relationship between theRoDand$\varepsilon$as well as between$\varepsilon$and$k$in our experimental results. To the best of our knowledge, this is the first study using the quantity of noise as a bridge to evaluateRoDfor multiple attributes (either numerical or binary data) and determine the relationship between differential privacy and$k$-anonymization.