Location-based service (LBS) is booming up in recent years with the rapid growth of mobile devices and the emerging of cloud computing paradigm. Among the challenges to establish LBS, the user privacy issue becomes the most important concern. A successful privacy-preserving LBS must be secure and provide accurate query [e.g., -nearest neighbor (NN)] results. In this work, we propose a private circular query protocol (PCQP) to deal with the privacy and the accuracy issues of privacy-preserving LBS. The protocol consists of a space filling curve and a public-key homomorphic cryptosystem. First, we connect the points of interest (POIs) on a map to form a circular structure with the aid of a Moore curve. And then the homomorphism of Paillier cryptosystem is used to perform secret circular shifts of POI-related information (POI-info), stored on the server side. Since the POI-info after shifting and the amount of shifts are encrypted, LBS providers (e.g., servers) have no knowledge about the user's location during the query process. The protocol can resist correlation attack and support a multiuser scenario as long as the predescribed secret circular shift is performed before each query; in other words, the robustness of the proposed protocol is the same as that of a one-time pad encryption scheme. As a result, the security level of the proposed protocol is close to perfect secrecy without the aid of a trusted third party and simulation results show that the k-NN query accuracy rate of the proposed protocol is higher than 90% even when is large.

Term suggestions recommend query terms to a user based on his initial query. Suggesting adequate terms is a challenging issue. Most existing commercial search engines suggest search terms based on the frequency of prior used terms that match the leading alphabets the user types. In this article, we present a novel mechanism to construct semantic term-relation graphs to suggest relevant search terms in the semantic level. We built term-relation graphs based on multipartite networks of existing social media, especially from Wikipedia. The multipartite linkage networks of contributor-term, term-category, and term-term are extracted from Wikipedia to eventually form term relation graphs. For fusing these multipartite linkage networks, we propose to incorporate the contributor-category networks to model the expertise of the contributors. Based on our experiments, this step has demonstrated clear enhancement on the accuracy of the inferred relatedness of the term-semantic graphs. Experiments on keyword-expanded search based on 200 TREC-5 ad-hoc topics showed obvious advantage of our algorithms over existing approaches.

In recommendation systems, a central host typically requires access to user profiles in order to generate useful recommendations. This access, however, undermines user privacy; the more information is revealed to the host, the more the user's privacy is compromised. In this paper, we propose a novel end-to-end encrypted recommendation mechanism which encrypts sensitive private data at the user end, without ever exposing plaintext private data to the host server. Unlike previously proposed privacy-preserving recommendation mechanisms, the data in this proposed system are lossless - a pivotal feature to many applications, e.g., in health informatics, business analytics, cyber security, etc. We achieve this goal by developing encrypted-domain polynomial ring homomorphism cryptographic algorithms to compute similarity of encrypted scores on the server, so that collaborative recommendations can be computed in the encryption domain and only an authorized person can decrypt the exact results. We also propose a novel key management system to make sure private information retrieval and recommendation computations can be executed in the encrypted domain in practice. Our experiments show that the proposed scheme offers robust security and lossless accurate recommendation, as well as high efficiency. Our preliminary results show the recommendation accuracy is 21% better than the existing statistical lossy privacy-preserving mechanisms based on random perturbation and user profile distribution. This new approach can potentially be applied to various data mining and cloud computing environments and significantly alleviates the privacy concerns of users.

This paper proposes an effective approach to provide relevant search terms for conceptual Web search. 'Semantic Term Suggestion' function has been included so that users can find the most appropriate query term to what they really need. Conventional approaches for term suggestion involve extracting frequently occurring key terms from retrieved documents. They must deal with term extraction difficulties and interference from irrelevant documents. In this paper, we propose a semantic term suggestion function called Collective Intelligence based Term Suggestion (CITS). CITS provides a novel social-network based framework for relevant terms suggestion with a semantic graph of the search term without limiting to the specific query term. A visualization of semantic graph is presented to the users to help browsing search results from related terms in the semantic graph. The search results are ranked each time according to their relevance to the related terms in the entire query session. Comparing to two popular commercial search engines, a user study of 18 users on 50 search terms showed better user satisfactions and indicated the potential usefulness of proposed method in real-world search applications.

In this paper, we propose a Semantic Graphs for Image Search (SGIS) system, which provides a novel way for image search by utilizing collaborative knowledge in Wikipedia and network analysis to form semantic graphs for search-term suggestion. The collaborative article editing process of Wikipediapsilas contributors is formalized as bipartite graphs that are folded into networks between terms. When user types in a search term, SGIS automatically retrieves an interactive semantic graph of related terms that allow users easily find related images not limited to a specific search term. Interactive semantic graph then serves as an interface to retrieve images through existing commercial search engines. This method significantly saves userspsila time by avoiding multiple search keywords that are usually required in generic search engines. It benefits both naive user who does not possess a large vocabulary (e.g., students) and professionals who look for images on a regular basis. In our experiments, 85% of the participants favored SGIS system than commercial search engines.

In this paper, we propose a Collaborative Knowledge Semantic Graphs Image Search (CKSGIS) system. It provides a novel way to conduct image search by utilizing the collaborative nature in Wikipedia and by performing network analysis to form semantic graphs for search-term expansion. The collaborative article editing process used by Wikipedia's contributors is formalized as bipartite graphs that are folded into networks between terms. When a user types in a search term, CKSGIS automatically retrieves an interactive semantic graph of related terms that allow users to easily find related images not limited to a specific search term. Interactive semantic graph then serve as an interface to retrieve images through existing commercial search engines. This method significantly saves users' time by avoiding multiple search keywords that are usually required in generic search engines. It benefits both naïve users who do not possess a large vocabulary and professionals who look for images on a regular basis. In our experiments, 85% of the participants favored CKSGIS system rather than commercial search engines.