Privacy Aspects of Recommender Systems

Friedman, Arik; Knijnenburg, Bart P.; Vanhecke, Kris; Martens, Luc; Berkovsky, Shlomo

doi:10.1007/978-1-4899-7637-6_19

Cited by 100 publications

(60 citation statements)

References 112 publications

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“…Firstly, using collaborative filtering requires to store every user's privacy preferences somewhere on a server and protection of users' preferences is complex [22,23]. With a content-based recommender system, user's preferences are stored locally and are not shared at all.…”

Section: Introduction To Decision Support Systemmentioning

confidence: 99%

A new approach for managing Android permissions: learning users’ preferences

Oglaza

Laborde

Zaraté

et al. 2017

EURASIP J. on Info. Security

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Today, permissions management solutions on mobile devices employ Identity Based Access Control (IBAC) models. If this approach was suitable when people had only a few games (like Snake or Tetris) installed on their mobile phones, the current situation is different. A survey from Google in 2013 showed that, on average, french users have installed 32 applications on their Android smartphones. As a result, these users must manage hundreds of permissions to protect their privacy. Scalability of IBAC is a well-known issue and many more advanced access control models have introduced abstractions to cope with this problem. However, such models are more complex to handle by non-technical users. Thus, we present a permission management system for Android devices that (1) learns users' privacy preferences with a novel learning algorithm, (2) proposes them abstract authorization rules, and (3) provides advanced features to manage these high-level rules. Our learning algorithm is compared to two other well-known approaches to show its efficiency. Finally, we prove this whole approach is more efficient than current permission management system by comparing it to Privacy Guard Manager.

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Section: Introduction To Decision Support Systemmentioning

confidence: 99%

A new approach for managing Android permissions: learning users’ preferences

Oglaza

Laborde

Zaraté

et al. 2017

EURASIP J. on Info. Security

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“…Firstly, using collaborative filtering requires storing every user's privacy preferences somewhere on a server. Protection of users' preferences is complex [16] [17]. With a content-based recommender system, user's preferences are stored locally and are not shared at all.…”

Section: A Introduction To Decision Support Systemmentioning

confidence: 99%

A Recommender-Based System for Assisting Non-technical Users in Managing Android Permissions

Oglaza¹,

Laborde

Benzekri

et al. 2016

2016 11th International Conference on Availability, Reliability and Security (ARES)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 18779The contribution was presented at ARES 2016 :http://www.ares-conference.eu/conference/ Abstract-Today, permissions management solutions on mobile devices employ Identity Based Access Control (IBAC) models. If this approach was suitable when people had only a few games (like Snake or Tetris) installed on their mobile phones, the current situation is different. A survey from Google in 2013 showed that, on average, US users have installed 33 applications on their Android smartphones. As a result, these users must manage hundreds of permissions to protect their privacy. Scalability of IBAC is a well-known issue and many more advanced access control models have introduced abstractions to cope with this problem. However, such models are more complex to handle by non-technical users. Thus, we present a permission management system for Android devices that 1) learns users' privacy preferences, 2) proposes them abstract authorization rules, and 3) provides advanced features to manage these high-level rules. We prove this approach is more efficient than current permission management system by comparing it to Privacy Guard Manager.

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“…Still, they only focus on particular problems in particular applications. Concerns over the lack of holistic approach have also been brought up by Friedman et al [65] who studied the privacy aspects of recommender systems -the subject that has drawn the most attention in the ethical discourse around the practices of big data. Another recent paper by Koene et al [107] points to the striking research imbalance in the area of personalized RS.…”

Section: Future Workmentioning

confidence: 99%

“…This makes the system immune to privacy threats that personalized services are prone to. The privacy implications have been thoroughly studied by Friedman et al [65] in the context of recommender systems. To derive a classification of recommendation ethics, it would help to study other moral issues beyond privacy and anonymization in the same context.…”

Section: From Algorithms To Ethicsmentioning

confidence: 99%

“…Sadly, both approaches fall victim of a "click to close" attitude from internet users [107]. It is worth noting, however, that the legislation pertaining to data collection, use, and disclosure has become more comprehensive in recent years with the introduction of the U.S. Consumer Privacy Bill of Rights and the European Privacy Directive [65].…”

Section: Data Collection and Filteringmentioning

confidence: 99%

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Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce

Paraschakis¹

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In memory of a beautiful soul ABSTRACTRecommender systems have become an integral part of virtually every e-commerce application on the web. The deployment of these expert systems has enabled users to quickly discover the products or services they need, at the same time increasing business revenues through better customer conversion. Remaining a very active research field since the mid-2000s, recommender systems have been modeled using a plethora of machine learning techniques. However, the adoptability of these models by industrial e-commerce platforms remains unclear.In this thesis, we assess the receptiveness of industrial platforms to algorithmic contributions of the research community by surveying more than 30 popular shopping cart solutions, and experimenting with various recommendation algorithms on proprietary e-commerce datasets.Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We provide a holistic view of these issues and summarize them in our ethical recommendation framework. This framework suggests new paradigm of ethics-awareness by design, and enables users to control sensitive moral aspects of recommendations via the proposed "ethical toolbox". The feasibility of this tool is supported by the results of our user study.Since the large part of moral implications stems from user profiling, we investigate algorithms capable of generating useragnostic recommendations based solely on a visited product page. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.

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Privacy Aspects of Recommender Systems

Cited by 100 publications

References 112 publications

A new approach for managing Android permissions: learning users’ preferences

A new approach for managing Android permissions: learning users’ preferences

A Recommender-Based System for Assisting Non-technical Users in Managing Android Permissions

Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce

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