Privacy-preserving collaborative recommendations based on random perturbations Skip to main content Home Research Outputs People Faculties, Schools & Groups Research Areas Accountancy Africa Ageing Agricultural Management Agriculture Agriculture & Food Animal Care Any Arts Arts & Humanities Biomedical Sciences Biomedical/Medical Sciences/Health Biotechnology & Informatics Business Administration Cancer & Neurodegeneration Cell Signalling Chemical Engineering Chemistry Civil Law Computer Science Computing Social Sciences Criminal Law Education Energy Engineering Fisheries Food Food Analysis Food Management Fossil Fuels Gene Expression Health Higher Education Housing Humanities Infectious Diseases Journalism Education Land Management Law Management & Commerce Mathematics Mechanical Engineering Medical Science Other Miscellaneous Categories Peace Studies Physics Renewable Energy Sources Science & Technology Seafood Vegetables Research Centres/Groups Air Quality Management Resource Centre Applied Marketing Research Group Applied Statistics Group Bat Conservation Research Lab Big Data Enterprise and Artificial Intelligence Laboratory Bristol Bio-Energy Centre Bristol Centre for Economics and Finance Bristol Centre for Linguistics Bristol Economic Analysis Bristol Group for Water Research Bristol Inter-disciplinary Group for Education Research Bristol Leadership and Change Centre Bristol Robotics Laboratory Centre for Appearance Research Centre for Applied Legal Research Centre for Architecture and Built Environment Research Centre for Fine Print Research Centre for Health and Clinical Research Centre for Machine Vision Centre for Moving Image Research Centre for Public Health and Wellbeing Centre for Research in Biosciences Centre for Sustainable Planning and Environments Centre for Transport and Society Centre for Water, Communities and Resilience Collaborative Entrepreneurship Research Group Commercial Law Research Unit Computer Science Research Centre Creative Technologies Laboratory Data Research Access and Governance Network (DRAGoN) Digital Cultures Research Centre Document and Location Research Group Education Innovation Centre Engineering Modelling and Simulation Research Group Environmental Law and Sustainability Research Group Global Crime, Justice and Security Research Group Human Resources, Work and Employment Innovation, Operations Management and Supply Institute for Sustainability, Health and Environment Institute of Bio-Sensing Technology Mathematics and Statistics Research Group Moving Image Research Group Psychological Sciences Research Group Regional History Centre Research Group in Mathematics and its Applications Robotic Engineering and Computing for Healthcare - FET Science Communication Unit Social Justice Research Group Social Science Research Group Software Engineering Research Group Sustainable Economies Research Group (SERG) The WHO Collaborating Centre for Healthy Urban Environment Unconventional Computing Group Visual Culture Research Group Browse By Year By Author By Type About OAI Research Repository All Output Person Project Advanced Search Privacy-preserving collaborative recommendations based on random perturbations Polatidis, Nikolaos; Georgiadis, Christos K.; Pimenidis, Elias; Mouratidis, Haralambos Home Outputs Authors Nikolaos Polatidis Christos K. Georgiadis Elias Pimenidis Elias.Pimenidis@uwe.ac.uk Senior Lecturer in Computer Science Haralambos Mouratidis Abstract © 2016 Elsevier Ltd Collaborative recommender systems offer a solution to the information overload problem found in online environments such as e-commerce. The use of collaborative filtering, the most widely used recommendation method, gives rise to potential privacy issues. In addition, the user ratings utilized in collaborative filtering systems to recommend products or services must be protected. The purpose of this research is to provide a solution to the privacy concerns of collaborative filtering users, while maintaining high accuracy of recommendations. This paper proposes a multi-level privacy-preserving method for collaborative filtering systems by perturbing each rating before it is submitted to the server. The perturbation method is based on multiple levels and different ranges of random values for each level. Before the submission of each rating, the privacy level and the perturbation range are selected randomly from a fixed range of privacy levels. The proposed privacy method has been experimentally evaluated with the results showing that with a small decrease of utility, user privacy can be protected, while the proposed approach offers practical and effective results. Citation Georgiadis, C. K., Polatidis, N., Georgiadis, C., Pimenidis, E., & Mouratidis, H. (2017). Privacy-preserving collaborative recommendations based on random perturbations. Expert Systems with Applications, 71, 18-25. https://doi.org/10.1016/j.eswa.2016.11.018 Journal Article Type Article Acceptance Date Nov 15, 2016 Online Publication Date Nov 18, 2016 Publication Date Apr 1, 2017 Journal Expert Systems with Applications Print ISSN 0957-4174 Publisher Elsevier Peer Reviewed Peer Reviewed Volume 71 Pages 18-25 DOI https://doi.org/10.1016/j.eswa.2016.11.018 Keywords collaborative filtering, random perturbations, multi-level privacy, recommender systems Public URL https://uwe-repository.worktribe.com/output/889764 Publisher URL http://dx.doi.org/10.1016/j.eswa.2016.11.018 Additional Information Additional Information : The final publication is available at http://dx.doi.org/10.1016/j.eswa.2016.11.018. 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