26-02-2013, 03:49 PM
Enabling Secure and Efficient Ranked Keyword Search over Outsourced Cloud Data
Enabling Secure.doc (Size: 29 KB / Downloads: 66)
Abstract
Search engine companies collect the “database of intentions”, the histories of their users’ search queries. To protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data deployment service a very challenging task. These search logs are a gold mine for researchers. Search engine companies, however, are wary of publishing search logs in order not to disclose sensitive information. In this paper we analyze algorithms for publishing frequent keywords, queries and clicks of a search log.
Ranked search greatly enhances system usability by enabling search result relevance ranking instead of sending un-differentiated results, and further ensures the file retrieval accuracy. The resulting design is able to facilitate efficient server-side ranking without losing keyword privacy. We then demonstrate that the stronger guarantee ensured by differential privacy unfortunately does not provide any utility for this problem. Our paper concludes with a large experimental study using real applications where we compare and previous work that achieves k-anonymity in search log publishing.
Existing System
We show that existing proposals to achieve anonymity in search logs are insufficient in the light of attackers who can actively influence the search log. However, we show that it is impossible to achieve good utility with differential privacy.
Disadvantages
Existing work on publishing frequent item sets often only tries to achieve anonymity or makes strong assumptions about the background knowledge of an attacker.
Proposed System
The main focus of this paper is search logs, our results apply to other scenarios as well. For example, consider a retailer who collects customer transactions. Each transaction consists of a basket of products together with their prices, and a time-stamp. In this case can be applied to publish frequently purchased products or sets of products. This information can also be used in a recommender system or in a market basket analysis to decide on the goods and promotions in a store.