Preserving privacy in collaborative filtering through distributed aggregation of offline profiles

Shokri, Reza; Pedarsani, Pedram; Theodorakopoulos, George; Hubaux, Jean-Pierre

doi:10.1145/1639714.1639741

Cited by 83 publications

(46 citation statements)

References 18 publications

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“…Alternatively, data modification can be conducted by the users. For example (Shokri, Pedarsani, Theodorakopoulos, & Hubaux, 2009) proposed to augment the user profile with profiles of similar users before sending the data to the centralized server. Thus, users keep their offline profiles and occasionally send it to the server, where the profiles are stored.…”

Section: Related Workmentioning

confidence: 99%

The impact of data obfuscation on the accuracy of collaborative filtering

Berkovsky

Kuflik

Ricci

2012

Expert Systems with Applications

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a b s t r a c tCollaborative filtering (CF) is a widely-used technique for generating personalized recommendations. CF systems are typically based on a central storage of user profiles, i.e., the ratings given by users to items. Such centralized storage introduces potential privacy breach, since all the user profiles may be accessible by untrusted parties when breaking the access control of the centralized system. Hence, recent studies have focused on enhancing the privacy of CF users by distributing their user profiles across multiple repositories and obfuscating the user profiles to partially hide the actual user ratings. This work combines these two techniques and investigates the unavoidable side effect of data obfuscation: the reduction of the accuracy of the generated CF predictions. The evaluation, which was conducted using three different datasets, shows that considerable parts of the user profiles can be modified without observing a substantial decrease of the CF prediction accuracy. The evaluation also indicates what parts of the user profiles are required for generating accurate CF predictions. In addition, we conducted an exploratory user study that reveals positive attitude of users towards the data obfuscation.Crown

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Section: Related Workmentioning

confidence: 99%

The impact of data obfuscation on the accuracy of collaborative filtering

Berkovsky

Kuflik

Ricci

2012

Expert Systems with Applications

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“…This is analogous to earlier work addressing queries on census data but, at that time, relatively few prospective attackers [2,7]. There has been some initial work towards retaining privacy while still benefiting from recommendation systems (e.g., [9,41]). There have also been other approaches such as k-anonymity [43], differential privacy [12], and applications of differential privacy to other domains [34,37].…”

Section: Privacymentioning

confidence: 66%

The tradeoffs of societal computing

Sheth

Kaiser

2011

Proceedings of the 10th SIGPLAN Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software

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As Social Computing has increasingly captivated the general public, it has become a popular research area for computer scientists. Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. We highlight some of the relevant research topics and open problems that we foresee in Societal Computing where software engineering research approaches and techniques seem particularly likely to be fruitful. We feel that these topics, and Societal Computing in general, need to gain prominence as they will provide useful avenues of research leading to increasing benefits for society as a whole.

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“…The data-obfuscation solutions (e.g. [24], [27], [37]) rely on adding noise to the original data or computation results to protect users' inputs. These solutions usually do not incur complicated manipulations on the users' inputs, so that they are much more efficient.…”

Section: A Related Workmentioning

confidence: 99%

Privacy-Preserving Friendship-Based Recommender Systems

Tang

Wang

2018

IEEE Trans. Dependable and Secure Comput.

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Privacy-preserving recommender systems have been an active research topic for many years. However, until today, it is still a challenge to design an efficient solution without involving a fully trusted third party or multiple semitrusted third parties. The key obstacle is the large underlying user populations (i.e. huge input size) in the systems. In this paper, we revisit the concept of friendship-based recommender systems, proposed by Jeckmans et al. and Tang and Wang. These solutions are very promising because recommendations are computed based on inputs from a very small subset of the overall user population (precisely, a user's friends and some randomly chosen strangers). We first clarify the single prediction protocol and Top-n protocol by Tang and Wang, by correcting some flaws and improving the efficiency of the single prediction protocol. We then design a decentralized single protocol by getting rid of the semi-honest service provider. In order to validate the designed protocols, we crawl Twitter and construct two datasets (FMT and 10-FMT) which are equipped with auxiliary friendship information. Based on 10-FMT and MovieLens 100k dataset with simulated friendships, we show that even if our protocols use a very small subset of the datasets, their accuracy can still be equal to or better than some baseline algorithm. Based on these datasets, we further demonstrate that the outputs of our protocols leak very small amount of information of the inputs, and the leakage decreases when the input size increases. We finally show that he single prediction protocol is quite efficient but the Top-n is not. However, we observe that the efficiency of the Top-n protocol can be dramatically improved if we slightly relax the desired security guarantee.

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Preserving privacy in collaborative filtering through distributed aggregation of offline profiles

Cited by 83 publications

References 18 publications

The impact of data obfuscation on the accuracy of collaborative filtering

The impact of data obfuscation on the accuracy of collaborative filtering

The tradeoffs of societal computing

Privacy-Preserving Friendship-Based Recommender Systems

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