Privacy risks in recommender systems

Ramakrishnan, Naren; Keller, Benjamin J.; Mirza, Batul J.; Grama, Ananth; Karypis, George

doi:10.1109/4236.968832

Cited by 158 publications

(88 citation statements)

References 8 publications

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“…This fact constitutes the above-mentioned schemes more complex, and of course vulnerable to attacks, as this additional information is required to be stored in a secure manner. Also, as reported in [24] anonymization can have severe undesirable outcomes if implemented incorrectly.…”

Section: At E(s)mentioning

confidence: 99%

A Privacy-Preserving Entropy-Driven Framework for Tracing DoS Attacks in VoIP

Tsiatsikas

Geneiatakis

Kambourakis

et al. 2013

2013 International Conference on Availability, Reliability and Security

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Abstract-Network audit trails, especially those composed of application layer data, can be a valuable source of information regarding the investigation of attack incidents. Nevertheless, the analysis of log files of large volume is usually both complex (slow) and privacy-neglecting. Especially, when it comes to VoIP, the literature on how audit trails can be exploited to identify attacks remains scarce. This paper provides an entropy-driven, privacypreserving, and practical framework for detecting resource consumption attacks in VoIP ecosystems. We extensively evaluate our framework under various attack scenarios involving single and multiple assailants. The results obtained show that the proposed scheme is capable of identifying malicious traffic with a false positive alarm rate up to 3.5%.

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Section: At E(s)mentioning

confidence: 99%

A Privacy-Preserving Entropy-Driven Framework for Tracing DoS Attacks in VoIP

Tsiatsikas

Geneiatakis

Kambourakis

et al. 2013

2013 International Conference on Availability, Reliability and Security

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“…In previous recommender system canny propose a model to protect the privacy of users based on a probalistic factor analysis model by using similar approach [3].Polat and Du suggest randomized technique [10,11].In this paper they use dummy set that cancels out result is good estimation of required output. In [5], Erkin put forward homomorphic encryption and multi party computation technique which gives secure data protection.…”

Section: Literature Surveymentioning

confidence: 99%

Flexible Dynamic Recommender System

Tupe¹,

Joshi²

2014

IOSRJCE

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“…All these attacks are realizations of threat 4.1 in Table 1. A more general model of the privacy threats related to recommendation systems is described in [23,20].…”

Section: Threats To Privacy In E-shoppingmentioning

confidence: 99%

Privacy Threats in E-Shopping (Position Paper)

Díaz

Choi

Arroyo

et al. 2016

Lecture Notes in Computer Science

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. E-shopping has grown considerably in the last years, providing customers with convenience, merchants with increased sales, and financial entities with an additional source of income. However, it may also be the source of serious threats to privacy. In this paper, we review the e-shopping process, discussing attacks or threats that have been analyzed in the literature for each of its stages. By showing that there exist threats to privacy in each of them, we argue our following position: "It is not enough to protect a single independent stage, as is usually done in privacy respectful proposals in this context. Rather, a complete solution is necessary spanning the overall process, dealing also with the required interconnections between stages." Our overview also reflects the diverse types of information that e-shopping manages, and the benefits (e.g., such as loyalty programs and fraud prevention) that system providers extract from them. This also endorses the need for solutions that, while privacy preserving, do not limit or remove these benefits, if we want prevent all the participating entities from rejecting it.

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Privacy risks in recommender systems

Abstract: Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries.

Cited by 158 publications

References 8 publications

A Privacy-Preserving Entropy-Driven Framework for Tracing DoS Attacks in VoIP

A Privacy-Preserving Entropy-Driven Framework for Tracing DoS Attacks in VoIP

Flexible Dynamic Recommender System

Privacy Threats in E-Shopping (Position Paper)

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