Frequency-based constraint relaxation for private query processing in cloud databases

We introduce a new definition of privacy based on query frequencies, as well as a frequency-based constraint relaxation methodology for private queries. Private queries undergo processing so that users may obtain data from a database in such a way that the user's search intentions, i.e. the data which the user is interested in, will be protected against exposure. Most existing protocols for private querying rely on the following two constraints to achieve privacy: i) queries are encoded so that the database server can handle query processes but cannot actually decode queries; ii) the server is forced to check all data in the server when computing query results. Because of these constraints, even database servers cannot distinguish which data are selected from the database. However, this second constraint compels servers to spend O(n) computational cost for each query processed, where n is the number of data entries on the server. We introduce a weaker privacy condition which ensures that search intentions are hidden within a portion of the database, as opposed to ordinary private queries which hide search intentions among all data in the database, and we argue that this definition of privacy is sufficient to combat attacks based on query frequencies. Our relaxation methodology relaxes the second constraint above and allows private querying while only examining a portion of the data in most cases. Our methodology is also flexible and applies not only to exact match queries in one dimensional data but also to range queries in one dimensional data and exact match queries in two dimensional data.