Yongjian Liao

Wen Huang, Shijie Zhou, Tianqing Zhu et al.

Due to successful applications of data analysis technologies in many fields, various institutions have accumulated a large amount of data to improve their services. As the speed of data collection has increased dramatically over the last few years, an increasing number of users are growing concerned about their personal information. Therefore, privacy preservation has become an urgent problem to be solved. Differential privacy as a strong privacy preservation tool has attracted significant attention. In this survey, we focus on improving utility of between differentially private mechanisms through technologies related to cryptography. In particular, we firstly focus on how to improve utility through anonymous communication. Then, we summarize how to improve utility by combining differentially private mechanisms with homomorphic encryption schemes. Next, we summarize hardness results of what is impossible to achieve for differentially private mechanisms' utility from the view of cryptography. Differential privacy borrowed intuitions from cryptography and still benefits from the progress of cryptography. To summarize the state-of-the-art and to benefit future researches, we are motivated to provide this survey.

Wen Huang, Shijie Zhou, Yongjian Liao

The differential privacy is a widely accepted conception of privacy preservation and the Laplace mechanism is a famous instance of differential privacy mechanisms to deal with numerical data. In this paper, we find that the differential privacy does not take liner property of queries into account, resulting in unexpected information leakage. In specific, the linear property makes it possible to divide one query into two queries such as $q(D)=q(D_1)+q(D_2)$ if $D=D_1\cup D_2$ and $D_1\cap D_2=\emptyset$. If attackers try to obtain an answer of $q(D)$, they not only can issue the query $q(D)$, but also can issue the $q(D_1)$ and calculate the $q(D_2)$ by themselves as long as they know $D_2$. By different divisions of one query, attackers can obtain multiple different answers for the query from differential privacy mechanisms. However, from attackers' perspective and from differential privacy mechanisms' perspective, the totally consumed privacy budget is different if divisions are delicately designed. The difference leads to unexpected information leakage because the privacy budget is the key parameter to control the amount of legally released information from differential privacy mechanisms. In order to demonstrate the unexpected information leakage, we present a membership inference attacks against the Laplace mechanism.