Privacy Analysis of Query-Set-Size Control

Abstract

Vast amounts of information of all types are collected daily about people by governments, corporations and individuals. The information is collected, for example, when users register to or use on-line applications, receive health related services, use their mobile phones, utilize search engines, or perform common daily activities. As a result, there is an enormous quantity of privately-owned records that describe individuals’ finances, interests, activities, and demographics. These records often include sensitive data and may violate the privacy of the users if published. The common approach to safeguarding user information is to limit access to the data by using an authentication and authorization protocol. However, in many cases the publication of user data for statistical analysis and research can be extremely beneficial for both academic and commercial uses, such as statistical research and recommendation systems. To maintain user privacy when such a publication occurs many databases employ anonymization techniques, either on the query results or the data itself. In this paper we examine and analyze the privacy offered for aggregate queries over a data structures representing linear topologies. Additionally, we offer a privacy probability measure, indicating the probability of an attacker to obtain information defined as sensitive by utilizing legitimate queries over such a system.