{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T10:13:56Z","timestamp":1781777636834,"version":"3.54.5"},"reference-count":103,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2023,3,17]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This study explores privacy challenges in recommender systems (RSs) and how they have leveraged privacy-preserving technology for risk mitigation. The study also elucidates the extent of adopting privacy-preserving RSs and postulates the future direction of research in RS security.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The study gathered articles from well-known databases such as SCOPUS, Web of Science and Google scholar. A systematic literature review using PRISMA was carried out on the 41 papers that are shortlisted for study. Two research questions were framed to carry out the review.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>It is evident from this study that privacy issues in the RS have been addressed with various techniques. However, many more challenges are expected while leveraging technology advancements for fine-tuning recommenders, and a research agenda has been devised by postulating future directions.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The study unveils a new comprehensive perspective regarding privacy preservation in recommenders. There is no promising study found that gathers techniques used for privacy protection. The study summarizes the research agenda, and it will be a good reference article for those who develop privacy-preserving RSs.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-02-2022-0083","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T07:11:52Z","timestamp":1651648312000},"page":"32-55","source":"Crossref","is-referenced-by-count":19,"title":["Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda"],"prefix":"10.1108","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3451-9794","authenticated-orcid":false,"given":"Dhanya","family":"Pramod","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"key2023031608342858100_ref001","doi-asserted-by":"publisher","article-title":"A critical survey on privacy prevailing in collaborative filtering recommender system: challenges, state-of-the-art methods and future directions","year":"2020","DOI":"10.1109\/ICEET48479.2020.9048206"},{"key":"key2023031608342858100_ref002","doi-asserted-by":"publisher","article-title":"Towards fully distributed and privacy-preserving recommendations via expert collaborative filtering and restful linked data","year":"2010","DOI":"10.1109\/WI-IAT.2010.53"},{"issue":"5","key":"key2023031608342858100_ref003","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s10207-007-0049-3","article-title":"Alambic: a privacy-preserving recommender system for electronic commerce","volume":"7","year":"2008","journal-title":"International Journal of Information Security"},{"issue":"3","key":"key2023031608342858100_ref004","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s41019-016-0020-2","article-title":"A practical privacy-preserving recommender system","volume":"1","year":"2016","journal-title":"Data Science and Engineering"},{"issue":"3","key":"key2023031608342858100_ref005","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1109\/TSC.2018.2839587","article-title":"Privacy preserving location-aware personalized web service recommendations","volume":"14","year":"2021","journal-title":"IEEE Transactions on Services Computing"},{"issue":"11","key":"key2023031608342858100_ref006","doi-asserted-by":"publisher","first-page":"42","DOI":"10.23919\/JCC.2021.11.004","article-title":"Privacy-preserving collaborative filtering algorithm based on local differential privacy","volume":"18","year":"2021","journal-title":"China Communications"},{"key":"key2023031608342858100_ref007","doi-asserted-by":"publisher","first-page":"110011","DOI":"10.1109\/ACCESS.2021.3101150","article-title":"Dynamic parameters-based reversible data Transform (RDT) algorithm in recommendation system","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"key2023031608342858100_ref008","doi-asserted-by":"publisher","article-title":"Genetic algorithm-based privacy preserving collaborative filtering","year":"2019","DOI":"10.1109\/ASYU48272.2019.8946389"},{"issue":"2","key":"key2023031608342858100_ref009","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1109\/TNSE.2020.3031179","article-title":"Integrating blockchain with artificial intelligence for privacy-preserving recommender systems","volume":"8","year":"2021","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"key2023031608342858100_ref010","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.ijhcs.2018.04.003","article-title":"The users' perspective on the privacy-utility trade-offs in health recommender systems","volume":"121","year":"2019","journal-title":"International Journal of Human Computer Studies"},{"issue":"5","key":"key2023031608342858100_ref011","doi-asserted-by":"publisher","DOI":"10.1145\/3394138","article-title":"Practical privacy preserving POI recommendation","volume":"11","year":"2020","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"key2023031608342858100_ref012","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3085562","article-title":"SecRec: a privacy-preserving method for the context-aware recommendation system","year":"2021","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"key2023031608342858100_ref013","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.eswa.2020.114366","article-title":"Differentially private user-based collaborative filtering recommendation based on k-means clustering","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"key2023031608342858100_ref014","first-page":"547","article-title":"A practical system for privacy-preserving collaborative filtering","year":"2012"},{"key":"key2023031608342858100_ref015","doi-asserted-by":"publisher","article-title":"An agent-based approach for privacy-preserving recommender systems","year":"2007","DOI":"10.1145\/1329125.1329345"},{"key":"key2023031608342858100_ref016","doi-asserted-by":"publisher","article-title":"A privacy-preserving recommender system for mobile commerce","year":"2015","DOI":"10.1109\/CNS.2015.7346905"},{"key":"key2023031608342858100_ref017","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107700","article-title":"Federated matrix factorization for privacy-preserving recommender systems","volume":"111","year":"2021","journal-title":"Applied Soft Computing"},{"key":"key2023031608342858100_ref018","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1145\/3298689.3347035","article-title":"PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy","year":"2019"},{"issue":"10","key":"key2023031608342858100_ref019","doi-asserted-by":"publisher","first-page":"9891","DOI":"10.1007\/s12652-020-02736-y","article-title":"PAPIR: privacy-aware personalized information retrieval","volume":"12","year":"2021","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"key2023031608342858100_ref020","doi-asserted-by":"publisher","article-title":"Privacy threat modeling in personalized search systems","year":"2022","DOI":"10.1007\/978-981-16-3637-0_22"},{"key":"key2023031608342858100_ref021","doi-asserted-by":"publisher","article-title":"Privacy-preserving recommender systems in dynamic environments","year":"2013","DOI":"10.1109\/WIFS.2013.6707795"},{"issue":"1","key":"key2023031608342858100_ref022","doi-asserted-by":"crossref","first-page":"32","DOI":"10.5120\/ijca2015905313","article-title":"Application of data mining in designing a recommender system on social networks","volume":"124","year":"2015","journal-title":"International Journal of Computer Applications"},{"key":"key2023031608342858100_ref023","doi-asserted-by":"crossref","first-page":"104325","DOI":"10.1016\/j.engappai.2021.104325","article-title":"Presentation a Trust Walker for rating prediction in recommender system with Biased Random Walk: effects of H-index centrality, similarity in items and friends","volume":"104","year":"2021","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"01","key":"key2023031608342858100_ref024","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1142\/S0219622020500522","article-title":"A hotel recommender system for tourists using the Artificial Bee Colony Algorithm and Fuzzy TOPSIS Model: a case study of tripadvisor","volume":"20","year":"2021","journal-title":"International Journal of Information Technology and Decision Making"},{"key":"key2023031608342858100_ref025","first-page":"1","article-title":"A hybrid method for recommendation systems based on tourism with an evolutionary algorithm and topsis model","year":"2022","journal-title":"Fuzzy Information and Engineering"},{"key":"key2023031608342858100_ref026","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1145\/3397271.3401053","article-title":"DPLCF: differentially private local collaborative filtering","year":"2020"},{"issue":"1","key":"key2023031608342858100_ref027","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1080\/0194262X.2020.1714529","article-title":"A bibliometric analysis of digital image forensics","volume":"39","year":"2020","journal-title":"Science and Technology Libraries"},{"key":"key2023031608342858100_ref028","doi-asserted-by":"crossref","first-page":"24746","DOI":"10.1109\/ACCESS.2020.2970576","article-title":"Smart contract privacy protection using AI in cyber-physical systems: tools, techniques and challenges","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"key2023031608342858100_ref029","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.future.2019.02.060","article-title":"Privacy preservation in blockchain based IoT systems: integration issues, prospects, challenges, and future research directions","volume":"97","year":"2019","journal-title":"Future Generation Computer Systems"},{"key":"key2023031608342858100_ref103","first-page":"1","article-title":"A survey of recommender systems for energy efficiency in buildings: principles, challenges and prospects","volume":"72","year":"2021"},{"key":"key2023031608342858100_ref104","article-title":"Blockchain-based recommender systems: applications, challenges and future opportunities","volume":"43","year":"2022","journal-title":"Computer Science Review"},{"key":"key2023031608342858100_ref030","doi-asserted-by":"publisher","article-title":"A locality sensitive hashing based approach for federated recommender system","year":"2020","DOI":"10.1109\/CCGrid49817.2020.000-1"},{"key":"key2023031608342858100_ref031","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.6233","article-title":"Differentially private locality sensitive hashing based federated recommender system","year":"2021","journal-title":"Concurrency Computation"},{"issue":"1","key":"key2023031608342858100_ref032","first-page":"012087","article-title":"Privacy protection for recommendation system: a survey","volume":"1325","year":"2019","journal-title":"Journal of Physics: Conference Series"},{"key":"key2023031608342858100_ref033","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.ins.2020.07.046","article-title":"Privacy-preserving point-of-interest recommendation based on geographical and social influence","volume":"543","year":"2021","journal-title":"Information Sciences"},{"key":"key2023031608342858100_ref034","first-page":"400","article-title":"September. Efficient privacy-enhanced familiarity-based recommender system","year":"2013"},{"key":"key2023031608342858100_ref035","doi-asserted-by":"crossref","unstructured":"Jeckmans, A.J., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R.L. and Tang, Q. (2013b), \u201cPrivacy in recommender systems\u201d, Social Media Retrieval, Springer, London, pp. 263-281.","DOI":"10.1007\/978-1-4471-4555-4_12"},{"key":"key2023031608342858100_ref036","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.ins.2018.12.085","article-title":"Towards a more reliable privacy-preserving recommender system","volume":"482","year":"2019","journal-title":"Information Sciences"},{"key":"key2023031608342858100_ref037","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/INTELCIS.2017.8260071","article-title":"Privacy preserving recommender system based on improved MASK and query restriction","year":"2017"},{"key":"key2023031608342858100_ref038","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-99010-1_55","article-title":"Adopting non-linear programming to select optimum privacy parameters for multi-parameters perturbation algorithm for data privacy improvement in recommender systems","volume":"845","year":"2019","journal-title":"Advances in Intelligent Systems and Computing"},{"issue":"15","key":"key2023031608342858100_ref039","doi-asserted-by":"publisher","first-page":"11425","DOI":"10.1007\/s00500-019-04605-z","article-title":"Feature selection by using privacy-preserving of recommendation systems based on collaborative filtering and mutual trust in social networks","volume":"24","year":"2020","journal-title":"Soft Computing"},{"key":"key2023031608342858100_ref040","doi-asserted-by":"publisher","article-title":"Study on privacy preserving recommender systems datasets","year":"2018","DOI":"10.1109\/ICICI.2017.8365367"},{"key":"key2023031608342858100_ref041","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/ICICI.2017.8365367","article-title":"Study on privacy preserving recommender systems datasets","year":"2018"},{"key":"key2023031608342858100_ref042","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.future.2018.03.017","article-title":"An efficient multi-party scheme for privacy preserving collaborative filtering for healthcare recommender system","volume":"86","year":"2018","journal-title":"Future Generation Computer Systems"},{"key":"key2023031608342858100_ref043","doi-asserted-by":"crossref","first-page":"66371","DOI":"10.1109\/ACCESS.2021.3076809","article-title":"Successive point-of-interest recommendation with local differential privacy","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"key2023031608342858100_ref044","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.eswa.2021.115871","article-title":"Utility-preserving differentially private skyline query","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"key2023031608342858100_ref045","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1145\/2063576.2063943","article-title":"YANA: an efficient privacy-preserving recommender system for online social communities","year":"2011"},{"key":"key2023031608342858100_ref046","doi-asserted-by":"publisher","article-title":"Pistis: a privacy-preserving content recommender system for online social communities","year":"2011","DOI":"10.1109\/WI-IAT.2011.136"},{"key":"key2023031608342858100_ref047","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1016\/j.neucom.2016.09.059","article-title":"Efficient privacy-preserving content recommendation for online social communities","volume":"219","year":"2017","journal-title":"Neurocomputing"},{"key":"key2023031608342858100_ref048","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6640667","article-title":"SDRM-LDP: a recommendation model based on local differential privacy","year":"2021","journal-title":"Wireless Communications and Mobile Computing"},{"key":"key2023031608342858100_ref049","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/CSP51677.2021.9357604","article-title":"A blockchain-based privacy-preserving recommendation mechanism","year":"2021"},{"key":"key2023031608342858100_ref050","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1145\/3460231.3478855","article-title":"FR-FMSS: federated recommendation via fake marks and secret sharing","year":"2021"},{"key":"key2023031608342858100_ref051","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55753-3_36","article-title":"When differential privacy meets randomized perturbation: a hybrid approach for privacy-preserving recommender system","volume":"10177","year":"2017","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"key2023031608342858100_ref052","doi-asserted-by":"publisher","article-title":"A security-assured accuracy-maximised privacy preserving collaborative filtering recommendation algorithm","year":"2015","DOI":"10.1145\/2790755.2790757"},{"key":"key2023031608342858100_ref053","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/ICBDA.2019.8713189","article-title":"Privacy-preserving recommendation for location-based services","year":"2019"},{"key":"key2023031608342858100_ref054","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112858","article-title":"A personal data store approach for recommender systems: enhancing privacy without sacrificing accuracy","volume":"139","year":"2020","journal-title":"Expert Systems with Applications"},{"issue":"7","key":"key2023031608342858100_ref055","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"6","author":"Prisma Group.","year":"2009","journal-title":"PLoS Med"},{"issue":"1","key":"key2023031608342858100_ref056","doi-asserted-by":"publisher","DOI":"10.1186\/s13635-016-0033-4","article-title":"An efficient privacy-preserving comparison protocol in smart metering systems","volume":"2016","year":"2016","journal-title":"Eurasip Journal on Information Security"},{"issue":"3","key":"key2023031608342858100_ref057","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.datak.2007.03.009","article-title":"Thoughts on k-anonymization","volume":"63","year":"2007","journal-title":"Data and Knowledge Engineering"},{"key":"key2023031608342858100_ref058","first-page":"1","article-title":"Self-determined reciprocal recommender systemwith strong privacy guarantees","year":"2021"},{"key":"key2023031608342858100_ref059","doi-asserted-by":"publisher","first-page":"91027","DOI":"10.1109\/ACCESS.2021.3091426","article-title":"Privacy-preserving matrix factorization for cross-domain recommendation","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"key2023031608342858100_ref060","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107213","article-title":"A privacy-preserving framework for cross-domain recommender systems","volume":"93","year":"2021","journal-title":"Computers and Electrical Engineering"},{"key":"key2023031608342858100_ref061","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-1304-6_9","article-title":"Towards privacy-preserving recommender system with blockchains","volume":"1123","year":"2019","journal-title":"Communications in Computer and Information Science"},{"key":"key2023031608342858100_ref062","doi-asserted-by":"publisher","article-title":"Measuring user-object interactions in IoT spaces","year":"2016","DOI":"10.1109\/RFID-TA.2015.7379797"},{"key":"key2023031608342858100_ref063","first-page":"625","article-title":"Privacy-preserving collaborative filtering using randomized perturbation techniques","year":"2003"},{"key":"key2023031608342858100_ref064","doi-asserted-by":"crossref","unstructured":"Polato, M. (2021), \u201cFederated variational autoencoder for collaborative filtering\u201d, doi: 10.1109\/IJCNN52387.2021.9533358.","DOI":"10.1109\/IJCNN52387.2021.9533358"},{"issue":"5","key":"key2023031608342858100_ref065","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1108\/BIJ-01-2021-0033","article-title":"Robotic process automation for industry: adoption status, benefits, challenges and research agenda","volume":"29","year":"2022","journal-title":"Benchmarking: An International Journal"},{"key":"key2023031608342858100_ref066","first-page":"1","article-title":"Assistive technology for elderly people: state of the art review and future research agenda","year":"2022","journal-title":"Science and Technology Libraries"},{"key":"key2023031608342858100_ref067","first-page":"409","article-title":"A peer-to-peer recommender system with privacy constraints","year":"2009"},{"key":"key2023031608342858100_ref068","first-page":"1","article-title":"Federated recommendation systems","year":"2019"},{"key":"key2023031608342858100_ref069","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1145\/3464298.3476130","article-title":"PProx: efficient privacy for recommendation-as-a-service","year":"2021"},{"key":"key2023031608342858100_ref070","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1145\/3318464.3380596","article-title":"Crypte: crypto-assisted differential privacy on untrusted servers","year":"2020"},{"key":"key2023031608342858100_ref071","unstructured":"Salem, R.B., A\u00efmeur, E. and Hage, H. (2021), \u201cThe privacy versus disclosure appetite dilemma: mitigation by recommendation\u201d."},{"key":"key2023031608342858100_ref072","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/4136909","article-title":"A secure recommendation system for providing context-aware physical activity classification for users","year":"2021","journal-title":"Security and Communication Networks"},{"key":"key2023031608342858100_ref073","first-page":"157","article-title":"October, Preserving privacy in collaborative filtering through distributed aggregation of offline profiles","year":"2009"},{"issue":"6","key":"key2023031608342858100_ref074","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102722","article-title":"Towards user-oriented privacy for recommender system data: a personalization-based approach to gender obfuscation for user profiles","volume":"58","year":"2021","journal-title":"Information Processing and Management"},{"key":"key2023031608342858100_ref075","first-page":"1","article-title":"A privacy settings recommender system for online social networks","year":"2014"},{"key":"key2023031608342858100_ref076","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1145\/3325773.3325787","article-title":"Location recommendation with privacy protection","year":"2019"},{"issue":"4","key":"key2023031608342858100_ref077","doi-asserted-by":"publisher","DOI":"10.2174\/1872212114999200807154801","article-title":"Iterative framework and privacy preservation in reciprocal recommendation","volume":"15","year":"2021","journal-title":"Recent Patents on Engineering"},{"issue":"5","key":"key2023031608342858100_ref078","first-page":"784","article-title":"Privacy-preserving friendship-based recommender systems","volume":"15","year":"2016","journal-title":"IEEE Transactions on Dependable and Secure Computing"},{"key":"key2023031608342858100_ref079","first-page":"565","article-title":"Personalized top-n sequential recommendation via convolutional sequence embedding","year":"2018"},{"key":"key2023031608342858100_ref080","doi-asserted-by":"publisher","DOI":"10.3233\/978-1-61499-900-3-373","article-title":"Adapting recommender systems to the new data privacy regulations","volume":"303","year":"2018","journal-title":"Frontiers in Artificial Intelligence and Applications"},{"key":"key2023031608342858100_ref081","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.jisa.2021.102830","article-title":"FSDS: a practical and fully secure document similarity search over encrypted data with lightweight client","year":"2021","journal-title":"Journal of Information Security and Applications"},{"key":"key2023031608342858100_ref082","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-020-00633-6","article-title":"Recommender systems in the healthcare domain: state-of-the-art and research issues","year":"2020","journal-title":"Journal of Intelligent Information Systems"},{"key":"key2023031608342858100_ref083","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.neucom.2020.10.014","article-title":"An efficient approach for privacy preserving decentralized deep learning models based on secure multi-party computation","volume":"422","year":"2021","journal-title":"Neurocomputing"},{"key":"key2023031608342858100_ref084","doi-asserted-by":"crossref","unstructured":"Verhaert, D., Nateghizad, M. and Erkin, Z. (2018), \u201cAn efficient privacy-preserving recommender system for e-healthcare systems\u201d, doi: 10.5220\/0006858501880199.","DOI":"10.5220\/0006858501880199"},{"key":"key2023031608342858100_ref085","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-7561-3_18","article-title":"An enhanced privacy-preserving recommender system","volume":"939","year":"2019","journal-title":"Communications in Computer and Information Science"},{"key":"key2023031608342858100_ref086","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-92571-0_15","article-title":"Faster private rating update via integer-based homomorphic encryption","volume":"13146","year":"2021","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"key2023031608342858100_ref087","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, J. and Wang, Y. (2016), \u201cTrust-aware privacy-preserving recommender system\u201d, doi: 10.4108\/eai.18-6-2016.2264146.","DOI":"10.4108\/eai.18-6-2016.2264146"},{"issue":"1","key":"key2023031608342858100_ref088","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.eng.2018.02.005","article-title":"Toward privacy-preserving personalized recommendation services","volume":"4","year":"2018","journal-title":"Engineering"},{"key":"key2023031608342858100_ref089","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.jisa.2019.03.009","article-title":"An efficient and privacy-preserving pre-clinical guide scheme for mobile eHealthcare","volume":"46","year":"2019","journal-title":"Journal of Information Security and Applications"},{"key":"key2023031608342858100_ref090","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.future.2018.05.077","article-title":"Private collaborative filtering under untrusted recommender server","volume":"109","year":"2020","journal-title":"Future Generation Computer Systems"},{"issue":"1","key":"key2023031608342858100_ref091","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1504\/ijbic.2020.108995","article-title":"A privacy-preserving recommendation method based on multi-objective optimisation for mobile users","volume":"16","year":"2020","journal-title":"International Journal of Bio-Inspired Computation"},{"key":"key2023031608342858100_ref092","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/1460234","article-title":"A differential privacy framework for collaborative filtering","year":"2019","journal-title":"Mathematical Problems in Engineering"},{"issue":"3","key":"key2023031608342858100_ref093","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1016\/j.ipm.2019.02.009","article-title":"Privacy-preserving multi-criteria collaborative filtering","volume":"56","year":"2019","journal-title":"Information Processing and Management"},{"key":"key2023031608342858100_ref094","doi-asserted-by":"publisher","first-page":"115717","DOI":"10.1109\/ACCESS.2020.3004250","article-title":"A privacy-preserving multi-task framework for knowledge graph enhanced recommendation","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"key2023031608342858100_ref095","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.knosys.2019.07.035","article-title":"Probabilistic matrix factorization with personalized differential privacy","year":"2019","journal-title":"Knowledge-Based System"},{"key":"key2023031608342858100_ref096","doi-asserted-by":"publisher","article-title":"Medical privacy-preserving service recommendation","year":"2020","DOI":"10.1109\/ICC40277.2020.9148641"},{"key":"key2023031608342858100_ref097","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yin, H., Chen, T., Huang, Z., Cui, L. and Zhang, X. (2021a), \u201cGraph embedding for recommendation against attribute inference attacks\u201d, doi: 10.1145\/3442381.3449813.","DOI":"10.1145\/3442381.3449813"},{"issue":"1","key":"key2023031608342858100_ref098","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MIS.2020.3005930","article-title":"Differentially private collaborative coupling learning for recommender systems","volume":"36","year":"2021","journal-title":"IEEE Intelligent Systems"},{"issue":"13","key":"key2023031608342858100_ref099","doi-asserted-by":"publisher","first-page":"10830","DOI":"10.1109\/JIOT.2021.3051060","article-title":"A privacy-preserving optimization of neighborhood-based recommendation for medical-aided diagnosis and treatment","volume":"8","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"key":"key2023031608342858100_ref100","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1109\/ICDMW.2018.00189","article-title":"A novel differential privacy recommendation method based on a distributed framework","year":"2019"},{"issue":"10","key":"key2023031608342858100_ref101","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.7544\/issn1000-1239.2019.20190541","article-title":"Research advances on privacy preserving in recommender systems","volume":"56","year":"2019","journal-title":"Jisuanji Yanjiu Yu Fazhan\/Computer Research and Development"}],"container-title":["Data Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-02-2022-0083\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-02-2022-0083\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:14:58Z","timestamp":1753398898000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/dta\/article\/57\/1\/32-55\/26217"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,4]]},"references-count":103,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,5,4]]},"published-print":{"date-parts":[[2023,3,17]]}},"alternative-id":["10.1108\/DTA-02-2022-0083"],"URL":"https:\/\/doi.org\/10.1108\/dta-02-2022-0083","relation":{},"ISSN":["2514-9288"],"issn-type":[{"value":"2514-9288","type":"print"}],"subject":[],"published":{"date-parts":[[2022,5,4]]}}}