An Information-Theoretic Privacy Criterion for Query Forgery in Information Retrieval

Rebollo-Monedero, David; Parra-Arnau, Javier; Forné, Jordi

doi:10.1007/978-3-642-27189-2_16

Cited by 14 publications

(21 citation statements)

References 28 publications

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“…Perturbation based techniques obfuscate user's interactions history by adding fake items and ratings to it. Rebollo et al [41] propose an information theoretic based privacy metric and then find the obfuscation rate for generating forged user profiles so that the privacy risk is minimized. Similarly, [37] proposes to add or remove items and ratings from user profiles minimize privacy risk.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

Beigi

Mosallanezhad

Guo

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which simultaneously recommends relevant items and counters private-attribute inference attacks. The key idea of our approach is to formulate this problem as an adversarial learning problem with two main components: the private attribute inference attacker, and the Bayesian personalized recommender. The attacker seeks to infer users' private-attribute information according to their items list and recommendations. The recommender aims to extract users' interests while employing the attacker to regularize the recommendation process. Experiments show that the proposed model both preserves the quality of recommendation service and protects users against private-attribute inference attacks. CCS CONCEPTS• Information systems → Recommender systems; • Security and privacy → Social network security and privacy; Privacy protections.

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Section: Related Workmentioning

confidence: 99%

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

Beigi

Mosallanezhad

Guo

et al. 2020

Proceedings of the 13th International Conference on Web Search and Data Mining

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“…Following this strategy, no third parties or external entities need to be trusted by the users to preserve their privacy. Existing approaches use different techniques and mechanisms and could be categorized mainly into three categories: cryptographic-based techniques [6,21,40,74,172], differential privacy-based approaches [66,76,89,120,128,130,166,197,198], and perturbation-based techniques [75,118,146,147,148,151,153,158,183] A group of works focus on providing cryptographic solutions to the problem of secure recommender systems. The approaches do not let the single trusted party have access to everyone's data [6,21,40,74,172].…”

Section: Recommendation Systems and Privacymentioning

confidence: 99%

“…Perturbation-based techniques usually obfuscate users item ratings by adding random noise to the user data. Rebollo et al [158] propose an approach that first measures the user's privacy risk as the KL divergence [48] between user's apparent profile and average population's distribution profile. The idea is that the more a user's profile diverges from the general population, the more information an attacker can learn about her.…”

Section: Perturbation-based Solutionsmentioning

confidence: 99%

A Survey on Privacy in Social Media

Beigi

Liu

2020

ACM/IMS Trans. Data Sci.

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The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. These data provide opportunities for researchers and service providers to study and better understand users' behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals' privacy. Users privacy in social media is an emerging research area and has attracted increasing attention recently. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users' privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups: (1) social graphs and privacy, (2) authors in social media and privacy, (3) profile attributes and privacy, (4) location and privacy, and (5) recommendation systems and privacy. We also discuss open problems and future research directions regarding user privacy issues in social media.

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“…In the absence of a specific statistical model on the frequency distribution of user profiles, as argued extensively in [9,11,26] on the basis of Jaynes' rationale for maximum entropy methods, we assume that anonymity risk may be adequately measured as the Kullback-Leibler (KL) divergence D(p q) between the user profile p and the population's q. The idea is that user profiles become less common as they diverge from the average of the population.…”

Section: An Information-theoretic Model For Measuring Anonymity Riskmentioning

confidence: 99%

On the Anonymity Risk of Time-Varying User Profiles

Puglisi¹,

Forné²,

Rebollo-Monedero³

2017

Preprint

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Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some online resource, new information is added to the user profile. Exposing private data does not only reveal information about single users' preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident in social networks where users receive suggestions based on their friends' activities. We propose an information-theoretic approach to measure the differential update of the anonymity risk of time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third-party services get to know about the users when a new activity is shared. We use actual Facebook data to show how our model can be applied to a real-world scenario.

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An Information-Theoretic Privacy Criterion for Query Forgery in Information Retrieval

Cited by 14 publications

References 28 publications

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

A Survey on Privacy in Social Media

On the Anonymity Risk of Time-Varying User Profiles

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