On the Viability of Contextual Advertising as a Privacy-Preserving Alternative to Behavioral Advertising on the Web

2021 ◽  
Author(s):  
Alexander Bleier
Keyword(s):  
Privacy Preserving ◽  
Contextual Advertising ◽  
The Web

Privacy Preserving Data Mining Services on the Web

2005 ◽  
pp. 246-255 ◽  
Author(s):  
Ayça Azgın Hintoğlu ◽  
Yücel Saygın ◽  
Salima Benbernou ◽  
Mohand Said Hacid
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining ◽  
The Web

Intelligent Techniques in Recommender Systems and Contextual Advertising

2013 ◽  
pp. 105-128 ◽  
Author(s):  
Giuliano Armano ◽  
Alessandro Giuliani ◽  
Eloisa Vargiu
Keyword(s):  
Recommender System ◽  
Information Filtering ◽  
Relevant Information ◽  
Web Page ◽  
Contextual Advertising ◽  
Generic Hybrid ◽  
Unified View ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
The Web

Information Filtering deals with the problem of selecting relevant information for a given user, according to her/his preferences and interests. In this chapter, the authors consider two ways of performing information filtering: recommendation and contextual advertising. In particular, they study and analyze them according to a unified view. In fact, the task of suggesting an advertisement to a Web page can be viewed as the task of recommending an item (the advertisement) to a user (the Web page), and vice versa. Starting from this insight, the authors propose a content-based recommender system based on a generic solution for contextual advertising and a hybrid contextual advertising system based on a generic hybrid recommender system. Relevant case studies have been considered (i.e., a photo recommender and a Web advertiser) with the goal of highlighting how the proposed approach works in practice. In both cases, results confirm the effectiveness of the proposed solutions.

ObSecure Logging (OSLo): A Framework to Protect and Evaluate the Web Search Privacy in Health Care Domain

2019 ◽  
Vol 9 (6) ◽  
pp. 1181-1190 ◽  
Author(s):  
Mohib Ullah ◽  
Muhammad Arshad Islam ◽  
Rafiullah Khan ◽  
Muhammad Aleem ◽  
Muhammad Azhar Iqbal
Keyword(s):  
Web Search ◽  
Medical Information ◽  
Privacy Preserving ◽  
Exposure Level ◽  
Query Log ◽  
Search Queries ◽  
Internet Users ◽  
Privacy Analysis ◽  
The Web ◽  
Better Than

Users around the world send queries to the Web Search Engine (WSE) to retrieve data from the Internet. Users usually take primary assistance relating to medical information from WSE via search queries. The search queries relating to diseases and treatment is contemplated to be the most personal facts about the user. The search queries often contain identifiable information that can be linked back to the originator, which can compromise the privacy of a user. In this work, we are proposing a distributed privacy-preserving protocol (OSLo) that eliminates limitation in the existing distributed privacy-preserving protocols and a framework, which evaluates the privacy of a user. The OSLo framework asses the local privacy relative to the group of users involved in forwarding query to the WSE and the profile privacy against the profiling of WSE. The privacy analysis shows that the local privacy of a user directly depends on the size of the group and inversely on the number of compromised users. We have performed experiments to evaluate the profile privacy of a user using a privacy metric Profile Exposure Level. The OSLo is simulated with a subset of 1000 users of the AOL query log. The results show that OSLo performs better than the benchmark privacy-preserving protocol on the basis of privacy and delay. Additionally, results depict that the privacy of a user depends on the size of the group.

Privacy-Preserving Data Mining on the Web

2010 ◽  
pp. 468-481
Author(s):  
Stanley R.M. Oliveira ◽  
Osmar R. Zaïane
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy And Security ◽  
Privacy Preserving Data Mining ◽  
The Right To Privacy ◽  
Security Research ◽  
Definition Of ◽  
The Web

Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era—the right to privacy. This chapter describes the foundations for further research in PPDM on the Web. In particular, we describe the problems we face in defining what information is private in data mining. We then describe the basis of PPDM including the historical roots, a discussion on how privacy can be violated in data mining, and the definition of privacy preservation in data mining based on users’ personal information and information concerning their collective activities. Subsequently, we introduce a taxonomy of the existing PPDM techniques and a discussion on how these techniques are applicable to Web-based applications. Finally, we suggest some privacy requirements that are related to industrial initiatives and point to some technical challenges as future research trends in PPDM on the Web.

Privacy-Preserving Data Mining on the Web

2008 ◽  
pp. 50-63 ◽  
Author(s):  
Stanley R.M. Oliveira ◽  
Osmar R. Zaiane
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy And Security ◽  
Privacy Preserving Data Mining ◽  
The Right To Privacy ◽  
Security Research ◽  
Definition Of ◽  
The Web

Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era—the right to privacy. This chapter describes the foundations for further research in PPDM on the Web. In particular, we describe the problems we face in defining what information is private in data mining. We then describe the basis of PPDM including the historical roots, a discussion on how privacy can be violated in data mining, and the definition of privacy preservation in data mining based on users’ personal information and information concerning their collective activities. Subsequently, we introduce a taxonomy of the existing PPDM techniques and a discussion on how these techniques are applicable to Web-based applications. Finally, we suggest some privacy requirements that are related to industrial initiatives and point to some technical challenges as future research trends in PPDM on the Web.

A privacy-preserving URL-hiding scheme for the web of things

2017 ◽  
Vol 23 (1/2) ◽  
pp. 15
Author(s):  
Jin Hyun Park ◽  
Im Young Jung ◽  
Soonja Kim
Keyword(s):  
Privacy Preserving ◽  
Web Of Things ◽  
The Web

Working at the web search engine side to generate privacy-preserving user profiles

2016 ◽  
Vol 64 ◽  
pp. 523-535 ◽  
Author(s):  
David Pàmies-Estrems ◽  
Jordi Castellà-Roca ◽  
Alexandre Viejo
Keyword(s):  
Search Engine ◽  
Web Search ◽  
Privacy Preserving ◽  
User Profiles ◽  
Web Search Engine ◽  
The Web

Privacy-Preserving & Incrementally-Deployable Support for Certificate Transparency in Tor

2021 ◽  
Vol 2021 (2) ◽  
pp. 194-213
Author(s):  
Rasmus Dahlberg ◽  
Tobias Pulls ◽  
Tom Ritter ◽  
Paul Syverson
Keyword(s):  
Privacy Preserving ◽  
Vital Role ◽  
Large Majority ◽  
Public Key ◽  
Web Browsers ◽  
Domain Name ◽  
Weakest Link ◽  
Security Indicators ◽  
Full Design ◽  
The Web

Abstract The security of the web improved greatly throughout the last couple of years. A large majority of the web is now served encrypted as part of HTTPS, and web browsers accordingly moved from positive to negative security indicators that warn the user if a connection is insecure. A secure connection requires that the server presents a valid certificate that binds the domain name in question to a public key. A certificate used to be valid if signed by a trusted Certificate Authority (CA), but web browsers like Google Chrome and Apple’s Safari have additionally started to mandate Certificate Transparency (CT) logging to overcome the weakest-link security of the CA ecosystem. Tor and the Firefox-based Tor Browser have yet to enforce CT. In this paper, we present privacy-preserving and incrementally-deployable designs that add support for CT in Tor. Our designs go beyond the currently deployed CT enforcements that are based on blind trust: if a user that uses Tor Browser is man-in-the-middled over HTTPS, we probabilistically detect and disclose cryptographic evidence of CA and/or CT log misbehavior. The first design increment allows Tor to play a vital role in the overall goal of CT: detect mis-issued certificates and hold CAs accountable. We achieve this by randomly cross-logging a subset of certificates into other CT logs. The final increments hold misbehaving CT logs accountable, initially assuming that some logs are benign and then without any such assumption. Given that the current CT deployment lacks strong mechanisms to verify if log operators play by the rules, exposing misbehavior is important for the web in general and not just Tor. The full design turns Tor into a system for maintaining a probabilistically-verified view of the CT log ecosystem available from Tor’s consensus. Each increment leading up to it preserves privacy due to and how we use Tor.

Privacy-Preserving Data Mining on the Web

2006 ◽  
pp. 282-301
Author(s):  
Stanley R. Oliveira ◽  
Osmar R. Zaiane
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy And Security ◽  
Privacy Preserving Data Mining ◽  
The Right To Privacy ◽  
Security Research ◽  
Definition Of ◽  
The Web

Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era—the right to privacy. This chapter describes the foundations for further research in PPDM on the Web. In particular, we describe the problems we face in defining what information is private in data mining. We then describe the basis of PPDM including the historical roots, a discussion on how privacy can be violated in data mining, and the definition of privacy preservation in data mining based on users’ personal information and information concerning their collective activities. Subsequently, we introduce a taxonomy of the existing PPDM techniques and a discussion on how these techniques are applicable to Web-based applications. Finally, we suggest some privacy requirements that are related to industrial initiatives and point to some technical challenges as future research trends in PPDM on the Web.

A privacy-preserving URL-hiding scheme for the web of things

2017 ◽  
Vol 23 (1/2) ◽  
pp. 15
Author(s):  
Jin Hyun Park ◽  
Im Young Jung ◽  
Soonja Kim
Keyword(s):  
Privacy Preserving ◽  
Web Of Things ◽  
The Web
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