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Decentralised Solutions for Preserving Privacy in Group Recommender Systems

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New Trends in Database and Information Systems (ADBIS 2023)

Abstract

Group Recommender Systems (GRS) combine large amounts of data from various user behaviour signals (likes, views, purchases) and contextual information to provide groups of users with accurate suggestions (e.g. rating prediction, rankings). To handle those large amounts of data, GRS can be extended to use distributed processing and storage solutions (e.g. MapReduce-like algorithms and NoSQL databases). As such, privacy has always been a core issue since most recommendation algorithms rely on user behaviour signals and contextual information that may contain sensitive information. However, existing work in this domain mostly distributes data processing tasks without addressing privacy, and the solutions that address privacy for GRS (e.g. k-anonymisation and local differential privacy) remain centralised. In this paper, we identify and analyse privacy concerns in GRS and provide guidelines on how decentralised techniques can be used to address them.

The author would like to thank Marko Tkalčič and Michael Mrissa for their help in elaborating the ideas developed in this paper.

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Notes

  1. 1.

    Information that can be used to differentiate or trace the identification of a person.

  2. 2.

    Privacy is the right to regulate or keep personal information secret [7].

  3. 3.

    I.e. a random transmission mechanism inside the group to prevent the main server from knowing users’ personal preferences.

  4. 4.

    I.e. masking member’s preferences in a pool of large data and masking their identity.

  5. 5.

    I.e. a combination of multiple centralised networks.

  6. 6.

    I.e. a network where every node has approximately the same number of connections to other nodes, and there is no hierarchy between nodes.

  7. 7.

    https://cassandra.apache.org/doc/latest/cassandra/architecture/overview.html.

  8. 8.

    I.e. each peer is endowed with a pair of public/private key.

  9. 9.

    GRSs are divided into two categories [16] based on the group preference and recommendation aggregation process.

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Acknowledgement

The authors gratefully acknowledge the European Commission for funding the InnoRenew CoE project (Grant Agreement #739574) under the Horizon2020 Widespread-Teaming program, the Republic of Slovenia (Investment funding of the Republic of Slovenia and the European Regional Development Fund), and the Slovenian Research Agency ARRS for funding the project J2-2504.

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Correspondence to Marina Paldauf .

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Paldauf, M. (2023). Decentralised Solutions for Preserving Privacy in Group Recommender Systems. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_48

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  • DOI: https://doi.org/10.1007/978-3-031-42941-5_48

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