Preventing Location-Based Identity Inference in Anonymous Spatial Queries

The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of Location-Based Services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to Location-Based Services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users

A Taxonomy of Hyperlink Hiding Techniques

Hidden links are designed solely for search engines rather than visitors. To get high search engine rankings, link hiding techniques are usually used for the profitability of black industries, such as illicit game servers, false medical services, illegal gambling, and less attractive high-profit industry, etc. This paper investigates hyperlink hiding techniques on the Web, and gives a detailed taxonomy. We believe the taxonomy can help develop appropriate countermeasures. Study on 5,583,451 Chinese sites' home pages indicate that link hidden techniques are very prevalent on the Web. We also tried to explore the attitude of Google towards link hiding spam by analyzing the PageRank values of relative links. The results show that more should be done to punish the hidden link spam.Comment: 12 pages, 2 figure