Using Search Results to Microaggregate Query Logs Semantically

Erola, Arnau; Roca, Jordi Castell

doi:10.1007/978-3-642-54568-9_10

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…There are different strategies to achieve this goal in the literature [20,[29][30][31], and we also have found in recent publications very similar approaches to the ones we had presented for the development of DisPA [32,33]. This shows a common interest of the research community towards the development of protocols that strive for a trade-off between utility and privacy in web search.…”

Section: Related Worksupporting

confidence: 68%

DisPA: An Intelligent Agent for Private Web Search

Juárez

Torra

2014

Studies in Computational Intelligence

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Abstract. Search queries can be used to infer preferences and interests of users. While search engines use this information for, among others, targeted advertising and personalization, these tasks can violate user's privacy. In 2006, after AOL disclosed the search queries of 650,000 users and some of them were re-identified, many Privacy Enhancement Technologies (PETs) have sought to solve this problem. The Dissociating Privacy Agent (DisPA), is a browser extension that acts as a proxy between the user and the search engine and semantically dissociates queries on real time. We show that DisPA increases the privacy of the user and hinders re-identification. We also propose an algorithm to measure and evaluate the privacy properties offered by DisPA.

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Section: Related Worksupporting

confidence: 68%

DisPA: An Intelligent Agent for Private Web Search

Juárez

Torra

2014

Studies in Computational Intelligence

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Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data

Strzelecki,

Miklosik

2024

AJIM

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PurposeThe landscape of search engine usage has evolved since the last known data were used to calculate click-through rate (CTR) values. The objective was to provide a replicable method for accessing data from the Google search engine using programmatic access and calculating CTR values from the retrieved data to show how the CTRs have changed since the last studies were published.Design/methodology/approachIn this study, the authors present the estimated CTR values in organic search results based on actual clicks and impressions data, and establish a protocol for collecting this data using Google programmatic access. For this study, the authors collected data on 416,386 clicks, 31,648,226 impressions and 8,861,416 daily queries.FindingsThe results show that CTRs have decreased from previously reported values in both academic research and industry benchmarks. The estimates indicate that the top-ranked result in Google's organic search results features a CTR of 9.28%, followed by 5.82 and 3.11% for positions two and three, respectively. The authors also demonstrate that CTRs vary across various types of devices. On desktop devices, the CTR decreases steadily with each lower ranking position. On smartphones, the CTR starts high but decreases rapidly, with an unprecedented increase from position 13 onwards. Tablets have the lowest and most variable CTR values.Practical implicationsThe theoretical implications include the generation of a current dataset on search engine results and user behavior, made available to the research community, creation of a unique methodology for generating new datasets and presenting the updated information on CTR trends. The managerial implications include the establishment of the need for businesses to focus on optimizing other forms of Google search results in addition to organic text results, and the possibility of application of this study's methodology to determine CTRs for their own websites.Originality/valueThis study provides a novel method to access real CTR data and estimates current CTRs for top organic Google search results, categorized by device.

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Privacy-Preserving Query Log Sharing Based on Prior N-Word Aggregation

Meng

Chen

et al. 2016

2016 IEEE Trustcom/BigDataSE/Ispa

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Using Search Results to Microaggregate Query Logs Semantically

Cited by 3 publications

References 11 publications

DisPA: An Intelligent Agent for Private Web Search

DisPA: An Intelligent Agent for Private Web Search

Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data

Privacy-Preserving Query Log Sharing Based on Prior N-Word Aggregation

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