{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T07:06:52Z","timestamp":1772176012896,"version":"3.50.1"},"reference-count":53,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2021,7,19]],"date-time":"2021-07-19T00:00:00Z","timestamp":1626652800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["LHT"],"published-print":{"date-parts":[[2022,5,27]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Similarly, Zhu <jats:italic>et al.<\/jats:italic> (2014) and Zhang <jats:italic>et al.<\/jats:italic> (2014) stated that addressing privacy concerns with the recommendation process is necessary for the healthy development of app recommendation. Recently, Xiao <jats:italic>et al.<\/jats:italic> (2020) mentioned that a lack of effective privacy policy hinders the development of personalized recommendation services. According to the reported work, privacy protection technology methods are too limited for mobile focusing on data encryption, anonymity, disturbance, elimination of redundant data to protect the recommendation process from privacy breaches. So, this situation motivated us to conduct a systematic literature review (SLR) to provide the viewpoint of privacy and security concerns as mentioned in current state-of-the-art in the mobile app recommendation domain.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In this work, the authors have followed Kitchenham guidelines (Kitchenham and Charters, 2007) to devise the SLR process. According to the guidelines, the SLR process has three main phases: (1) define, (2) conduct the search and (3) report the results. Furthermore, the authors used systematic mapping approach as well to ensure the whole process.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>Based on the selected studies, the authors proposed three main thematic taxonomies, including architectural style, security and privacy strategies, and user-usage in the mobile app recommendation domain. From the studies' synthesis viewpoint, it is observed that the majority of the research efforts have focused on the movie recommendation field, while the mainly used privacy scheme is homomorphic encryption. Finally, the authors suggested a set of future research dimensions useful for the potential researchers interested to perform the research in the mobile app recommendation domain.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>This is an SLR article, based on existing published research, where the authors identified key issues and future directions.<\/jats:p><\/jats:sec>","DOI":"10.1108\/lht-04-2021-0147","type":"journal-article","created":{"date-parts":[[2021,7,16]],"date-time":"2021-07-16T11:47:43Z","timestamp":1626436063000},"page":"725-749","source":"Crossref","is-referenced-by-count":14,"title":["Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review"],"prefix":"10.1108","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4738-9127","authenticated-orcid":false,"given":"Saira","family":"Beg","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9643-6858","authenticated-orcid":false,"given":"Saif Ur Rehman","family":"Khan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5083-0019","authenticated-orcid":false,"given":"Adeel","family":"Anjum","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"key2022052603103684900_ref001","volume-title":"User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy","year":"2018"},{"key":"key2022052603103684900_ref002","article-title":"Recommender systems: a systematic review of the state of the art literature and suggestions for future research","year":"2018","journal-title":"Kybernetes"},{"key":"key2022052603103684900_ref003","first-page":"8887","article-title":"A systematic literature review of recommender systems for requirements engineering\u2019","volume":"975","year":"2020","journal-title":"International Journal of Computer Applications"},{"key":"key2022052603103684900_ref004","first-page":"1","article-title":"S-box design based on optimize LFT parameter selection: a practical approach in recommendation system domain","volume-title":"Multimedia Tools and Applications","year":"2020"},{"issue":"08","key":"key2022052603103684900_ref005","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1142\/S0218194013500320","article-title":"A survey of privacy-preserving collaborative filtering schemes","volume":"23","year":"2013","journal-title":"International Journal of Software Engineering and Knowledge Engineering"},{"key":"key2022052603103684900_ref006","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.cose.2017.10.015","article-title":"Taxonomy of mobile users' security awareness","volume":"73","year":"2018","journal-title":"Computers and Security"},{"key":"key2022052603103684900_ref007","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/SP.2014.15","article-title":"Using frankencerts for automated adversarial testing of certificate validation in SSL\/TLS implementations","volume-title":"2014 IEEE Symposium on Security and Privacy","year":"2014"},{"key":"key2022052603103684900_ref008","first-page":"490","article-title":"MobHide: app-level runtime data anonymization on mobile","year":"2020"},{"key":"key2022052603103684900_ref009","first-page":"217","article-title":"A framework for estimating privacy risk scores of mobile apps","year":"2020"},{"key":"key2022052603103684900_ref010","first-page":"137","article-title":"Smartphone: the ultimate IoT and IoE device","volume-title":"Smartphones from an Applied Research Perspective","year":"2017"},{"key":"key2022052603103684900_ref011","volume-title":"Survey of Privacy-Preserving Collaborative Filtering","year":"2020"},{"key":"key2022052603103684900_ref015","first-page":"8043905","article-title":"Recommendation and classification systems: a systematic mapping study","volume":"2019","year":"2019","journal-title":"Scientific Programming"},{"issue":"3","key":"key2022052603103684900_ref012","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/TIFS.2012.2190726","article-title":"Generating private recommendations efficiently using homomorphic encryption and data packing","volume":"7","year":"2012","journal-title":"IEEE Transactions on Information Forensics and Security"},{"key":"key2022052603103684900_ref013","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/978-1-4899-7637-6_19","article-title":"Privacy aspects of recommender systems","volume-title":"Recommender Systems Handbook","year":"2015"},{"key":"key2022052603103684900_ref014","first-page":"961","article-title":"DPLCF: differentially private local collaborative filtering","year":"2020"},{"key":"key2022052603103684900_ref016","first-page":"266","article-title":"What's inside my app? 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