Releasing search queries and clicks privately

Korolova, Aleksandra; Kenthapadi, Krishnaram; Mishra, Nina; Ntoulas, Alexandros

doi:10.1145/1526709.1526733

Cited by 186 publications

(204 citation statements)

References 14 publications

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“…Click logs and/or query logs were also researched for Google and Yahoo search engines on cell phones [36]. Given a large query click log [26], click models provide a principled approach to inferring user-perceived relevance of web documents. There are click models as the click chain model [16] based on a solid Bayesian framework, the ImpressionRanks [5], the contextual advertising [25,43], the DBN [10], UBM [13], BBM [27] and GCM [49].…”

Section: Related Workmentioning

confidence: 99%

Semantic Search on Applicant Tracking System

Ha¹,

Rahman²

2017

International Journal of Advanced Research in Computer and Comm

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Web search engines often federate many user queries to relevant structured databases. For example, a recruitment-related query might be federated to a jobseekers-and-employers database containing their resumes and skills. The relevant structured data items are then returned to the user along with web search results. Though each structured database is searched in isolation, the search often produces empty / incomplete results as the database may not contain the required information to answer the query. Starting from our Applicant Tracking System (ATS), we have 16 development databases of over 650,000 profile documents of resumes / cover letters / skills. There are on average 238 keywords per document. In fact, per minute there can be up to 200,000 transactions within all these databases. Our existing traditional database search technique (by full-text keyword PostgreSQL search) can be frozen or taking very long to respond unless if we cut off / only search from top profiles, we will not have the search results ready by thirty seconds, but this cut-off limitation returned incorrect results; for example for a query "Jet fuel Thermal Oxidation" to request information about job seekers whose resumes contain skills in Oil and Gas industry, in the top ten results there was a conflict in relevance ranking. In order to research a more suitable full-text keyword search technique better than the existing database search, we considered employment of semantic search models. Our semantic search technique has 88% -91.22% accuracy with very much quicker queries that can help users to make a search of 4 keywords of skills completed from 1 second to 28 seconds. Furthermore, the semantic search engine becomes very strong that users can search by entering a whole text paragraph. We designed a combination of semantic search that look for web pages per search and database search.

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Section: Related Workmentioning

confidence: 99%

Semantic Search on Applicant Tracking System

Ha¹,

Rahman²

2017

International Journal of Advanced Research in Computer and Comm

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“…[43] argues that, for DP guarantees to be met, δ < 1/c, where c represents the number of clients answering this query. Since in our setting a client may answer the same query multiple times, we require δ < 1/(m × c), where m represents the maximum number of queries a client can answer.…”

Section: Noise At the Publishermentioning

confidence: 99%

Non-tracking web analytics

Akkuş

Chen

Hardt

et al. 2012

Proceedings of the 2012 ACM Conference on Computer and Communications Security

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Today, websites commonly use third party web analytics services to obtain aggregate information about users that visit their sites. This information includes demographics and visits to other sites as well as user behavior within their own sites. Unfortunately, to obtain this aggregate information, web analytics services track individual user browsing behavior across the web. This violation of user privacy has been strongly criticized, resulting in tools that block such tracking as well as anti-tracking legislation and standards such as Do-Not-Track. These efforts, while improving user privacy, degrade the quality of web analytics. This paper presents the first design of a system that provides web analytics without tracking. The system gives users differential privacy guarantees, can provide better quality analytics than current services, requires no new organizational players, and is practical to deploy. This paper describes and analyzes the design, gives performance benchmarks, and presents our implementation and deployment across several hundred users.

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“…There have been also a few recent works that specifically study anonymizing query logs [1,21,12,10,20]. Notably, [13,11] have demonstrated the ineffectiveness or privacy risks of naive anonymization schemes such as token based hashing and simple bundling.…”

Section: Related Workmentioning

confidence: 99%

“…Notably, [13,11] have demonstrated the ineffectiveness or privacy risks of naive anonymization schemes such as token based hashing and simple bundling. [12] provides an anonymization technique with rigorous differential privacy guarantee, however, the utility of the anonymized data is limited. [10] defines an interesting notion, k δ -anonymity, that addresses the sparsity of the query terms but does not prevent the linking attack between an individual and potentially sensitive queries.…”

Section: Related Workmentioning

confidence: 99%

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Personalized Web Search

SpringerReference

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We study the problem of anonymizing user profiles so that user privacy is sufficiently protected while the anonymized profiles are still effective in enabling personalized web search. We propose a Bayes-optimal privacy based principle to bound the prior and posterior probability of associating a user with an individual term in the anonymized user profile set. We also propose a novel bundling technique that clusters user profiles into groups by taking into account the semantic relationships between the terms while satisfying the privacy constraint. We evaluate our approach through a set of preliminary experiments using real data demonstrating its feasibility and effectiveness.

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Releasing search queries and clicks privately

Cited by 186 publications

References 14 publications

Semantic Search on Applicant Tracking System

Semantic Search on Applicant Tracking System

Non-tracking web analytics

Personalized Web Search

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