Releasing search queries and clicks privately

By A. Korolova, Krishnaram Kenthapadi, Nina Mishra, Alexandros Ntoulas. 2009

277 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

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Citations in the corpus

BLENDER: Enabling Local Search with a Hybrid Differential Privacy Model (2017, methodology): “For example, [24, 26, 16, 31] address the problem of publishing a subset of the data contained in a search log with differential privacy guarantees; [27] and [6] propose approaches for frequent item identification; [14] propose an approach for monitoring aggregated web browsing activities; and so on”
Functional Mechanism: Regression Analysis under Differential Privacy (2012, background): “This relaxed notion is useful in the scenarios where ϵ -differential privacy is too strict to allow any meaning results to be released (see [12, 15] for examples).”
Privacy via the Johnson-Lindenstrauss Transform (2012, methodology): “On the other hand, approaches to data sharing such as the recently proposed provably private methods for search query and click data release [16,20] accomplish it at the price of giving up on all user-level information.”
Differential privacy via wavelet transforms (2009, background): “Numerous techniques have been proposed for ensuring differential privacy in data publishing [6], [15]–[23].”
Differential Privacy via Wavelet Transforms (2009, background): “[19], [20], [21] for data publishing algorithms based on similar ideas.”
The application of differential privacy to health data (2012, background): “However these studies concentrate on particular kinds of data such as search logs [28], [34], on specific applications such as recommender systems [38], and record linkage [31], or on certain type of low sensitivity queries [23], [43], [45].”
Privacy-Preserving Query Log Sharing Based on Prior N-Word Aggregation (2016, background): “[5] proved the differential privacy guarantee being achieved by sampling the first d records of each user.”
Differentially private publication of location entropy (2016, methodology): “Improving Baseline by Limiting User Activity: To reduce the global sensitivity of LE, and inspired by [13], we propose a thresholding technique, named Limit , to limit an individual’s activity by truncating C and M .”
PrivBasis: Frequent Itemset Mining with Differential Privacy (2012, background): “) Work on releasing differentially private private search logs, including the AOL search log dataset, has been addressed in [26] and [21].”
Differentially private histogram publication (2012, unknown): “[19] and Götz et al.”
Non-tracking web analytics (2012, background): “[43] argues that, for DP guarantees to be met, δ < 1/c, where c represents the number of clients answering this query.”
Protecting privacy when mining and sharing user data (2012, background): “The preliminary version of the work has appeared in the 18th International World Wide Web Conference [110], where it was one of six nominees for the Best Paper Award2.”
A Practical Application of Differential Privacy to Personalized Online Advertising (2011, background): “∗This work was supported by a starting grant from the European Research Council.”
Monitoring web browsing behavior with differential privacy (2014, background): “Since then, various works have adopted this deﬁnition for publishing histograms [27], search logs [18], mining data streams [6], and record linkage [3].”
Privacy-Preserving Data Exploration in Genome-Wide Association Studies (2013, methodology): “It has been successfully applied to search logs [16], movie-viewing records [21], network traces [20], and social networks [11].”
I Have a DREAM! (DiffeRentially privatE smArt Metering) (2011, background): “Several prominent e xamples in the history have shown that ad-hoc methods do not work [12].”