A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users

Privacy Protection in Web Search

This paper presents web search has demonstrated in improving the quality of various search services on the internet, user reluctance to disclose the private information during search has become major barrier for the wide proliferation of password. Protection in password authentication model user preferences as hierarchical user profiles, a password framework know as user profile search that can adaptively generalize profile by search query while respecting user specified privacy requirements. Our work provides utility of personalization and the privacy risk of exposing the generalized profile using Greedy algorithm is a method for deciding whether personalizing a query is efficient

Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions

Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Survey on Privacy Preservation in Personalized Web Environment

Personalized web search (PWS) is a general category of search techniques aiming at providing different search results for different users or organize search results differently for each user, based upon their interest, preferences and information needs. As the expense, user information has to be collected and analyzed to figure out the user intention behind the issued query. However, users are uncomfortable with exposing private information during search which has become a major barrier for the wide proliferation of PWS. Search engines should provide security mechanism such that user will be ensured of its privacy and its information should be kept safe. Many personalization techniques are giving access to achieve personalization of user’s web search. Search engines can provide more accurate and specific data if users trust search engine and provide more information. But users should be ensured that their private information should be kept safe. In this paper we will discuss on different techniques on personalized web search and securing personalized information. DOI: 10.17762/ijritcc2321-8169.16041

Survey on privacy preservation in personalized web environment

Personalized web search (PWS) is a general category of search techniques aiming at providing different search results for different users or organize search results differently for each user, based upon their interest, preferences and information needs. As the expense, user information has to be collected and analyzed to figure out the user intention behind the issued query. However, users are uncomfortable with exposing private information during search which has become a major barrier for the wide proliferation of PWS. Search engines should provide security mechanism such that user will be ensured of its privacy and its information should be kept safe. Many personalization techniques are giving access to achieve personalization of user’s web search. Search engines can provide more accurate and specific data if users trust search engine and provide more information. But users should be ensured that their private information should be kept safe. In this paper we will discuss on different techniques on personalized web search and securing personalized information. DOI: 10.17762/ijritcc2321-8169.16040