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

A Privacy Protection in Personalized Web Search for Knowledge Mining: A Survey

The web search engines (e.g. Google, Yahoo etc.) help the users to find required useful information on the World Wide Web (WWW). But it has become increasingly difficult to get the expected results from the web search engine because contentsare available in web is very vast and ambiguous.Due to tremendous data opportunities in the internet, the privacy protection is very essential to preserve user search behaviors and their profiles. In this paper system present a novel protocol specially designed to protect the users’ privacy in front of web search profiling. Personalized web search (PWS) has demonstrated its effectiveness in improving the quality of various search services on the Internet. Our runtime generalization aims at striking a balance between two predictive metrics that evaluate the utility of personalization and the privacy risk of exposing the generalized profile. System proposed two greedy algorithms namely GreedyDP and GreedyIL. These two algorithms are used for runtime generalization.The proposed protocol preserves the privacy of the individuals who deal with a web search engine.System provides a distorted user profile to the web search engine. It offers implementation details and computational and communication results that show that the proposed protocol improves the existing solutions in terms of query delay

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

Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft