{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:12:11Z","timestamp":1760242331967,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,5,1]],"date-time":"2017-05-01T00:00:00Z","timestamp":1493596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Online Behavioural Advertising (OBA) enables promotion companies to effectively target users with ads that best satisfy their purchasing needs. This is highly beneficial for both vendors and publishers who are the owners of the advertising platforms, such as websites and app developers, but at the same time creates a serious privacy threat for users who expose their consumer interests. In this paper, we categorize the available ad-distribution methods and identify their limitations in terms of security, privacy, targeting effectiveness and practicality. We contribute our own system, which utilizes opportunistic networking in order to distribute targeted adverts within a social network. We improve upon previous work by eliminating the need for trust among the users (network nodes) while at the same time achieving low memory and bandwidth overhead, which are inherent problems of many opportunistic networks. Our protocol accomplishes this by identifying similarities between the consumer interests of users and then allows them to share access to the same adverts, which need to be downloaded only once. Although the same ads may be viewed by multiple users, privacy is preserved as the users do not learn each other\u2019s advertising interests. An additional contribution is that malicious users cannot alter the ads in order to spread malicious content, and also, they cannot launch impersonation attacks.<\/jats:p>","DOI":"10.3390\/fi9020016","type":"journal-article","created":{"date-parts":[[2017,5,2]],"date-time":"2017-05-02T11:37:20Z","timestamp":1493725040000},"page":"16","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Private and Secure Distribution of Targeted Advertisements to Mobile Phones"],"prefix":"10.3390","volume":"9","author":[{"given":"Stylianos","family":"Mamais","sequence":"first","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University, 5 The Parade, Roath, Cardiff CF24 3AA, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2701-7809","authenticated-orcid":false,"given":"George","family":"Theodorakopoulos","sequence":"additional","affiliation":[{"name":"School of Computer Science and Informatics, Cardiff University, 5 The Parade, Roath, Cardiff CF24 3AA, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,1]]},"reference":[{"key":"ref_1","unstructured":"eMarketer (2017, January 18). Mobile to Account for More than Half of Digital Ad Spending in 2015. Available online: https:\/\/www.emarketer.com\/Article\/Mobile-Account-More-than-Half-of-Digital-Ad-Spending-2015\/1012930."},{"key":"ref_2","unstructured":"The Guardian (2017, January 18). UK Mobile Ad Spend \u2019To Overtake Print and TV\u2019. Available online: https:\/\/www.theguardian.com\/media\/2015\/sep\/30\/mobile-advertising-spend-print-tv-emarketer."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y., and Chen, Z. (2009, January 20\u201324). How much can behavioural targeting help online advertising?. Proceedings of the 18th International Conference on World Wide Web, Madrid, Spain.","DOI":"10.1145\/1526709.1526745"},{"key":"ref_4","unstructured":"Purcell, K., Brenner, J., and Rainie, L. (2017, January 18). Pew Research Center: Search Engine Use 2012. Available online: http:\/\/www.pewinternet.org\/2012\/03\/09\/search-engine-use-2012\/."},{"key":"ref_5","unstructured":"Federal Trade Commission (FTC) (2017, January 18). FTC Staff Report: Self-Regulatory Principles for Online Behavioural Advertising: Tracking, Targeting, and Technology, Available online: https:\/\/www.ftc.gov\/sites\/default\/files\/documents\/reports\/federal-trade-commission-staff-report-self-regulatory-principles-online-behavioural-advertising\/p085400behavadreport.pdf."},{"key":"ref_6","unstructured":"Sun, Y., and Ji, G. (2010). Privacy preserving in personalized mobile marketing. Active Media Technology: 6th International Conference, AMT 2010, Toronto, Canada, August 28\u201330, 2010 Proceedings, Springer."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Backes, M., Kate, A., Maffei, M., and Pecina, K. (2012, January 21\u201323). Obliviad: Provably secure and practical online behavioural advertising. Proceedings of the 2012 IEEE Symposium on Security and Privacy, San Francisco, CA, USA.","DOI":"10.1109\/SP.2012.25"},{"key":"ref_8","unstructured":"Chor, B., Goldreich, O., Kushilevitz, E., and Sudan, M. (1995, January 23\u201325). Private information retrieval. Proceedings of the IEEE 36th Annual Symposium on Foundations of Computer Science, Milwaukee, WI, USA."},{"key":"ref_9","unstructured":"Toubiana, V., Narayanan, A., Boneh, D., Nissenbaum, H., and Barocas, S. (March, January 28). Adnostic: Privacy preserving targeted advertising. Proceedings of the Network and Distributed System Symposium, San Diego, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kodialam, M., Lakshman, T., and Mukherjee, S. (2012, January 25\u201330). Effective ad targeting with concealed profiles. Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), Orlando, FL USA.","DOI":"10.1109\/INFCOM.2012.6195609"},{"key":"ref_11","unstructured":"Guha, S., Cheng, B., and Francis, P. (April, January 30). Privad: Practical privacy in online advertising. Proceedings of the 8th USENIX conference on Networked Systems Design and Implementation, Boston, MA, USA."},{"key":"ref_12","unstructured":"Carrara, L., and Orsi, G. (2011). A New Perspective in Pervasive Advertising, Department of Computer Science, University of Oxford. Technical Report."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Carrara, L., Orsi, G., and Tanca, L. (2013, January 27\u201329). Semantic pervasive advertising. Proceedings of the 7th International Conference on Web Reasoning and Rule Systems, Mannheim, Germany.","DOI":"10.1007\/978-3-642-39666-3_18"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Straub, T., and Heinemann, A. (2004, January 14\u201317). An anonymous bonus point system for mobile commerce based on word-of-mouth recommendation. Proceedings of the 2004 ACM Symposium on Applied Computing, Nicosia, Cyprus.","DOI":"10.1145\/967900.968059"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ratsimor, O., Finin, T., Joshi, A., and Yesha, Y. (2003). eNcentive: A framework for intelligent marketing in mobile peer-to-peer environments. The 5th international conference on Electronic Commerce (ICEC 2003), ACM.","DOI":"10.1145\/948005.948017"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.tele.2014.12.002","article-title":"Let\u2019s Meet! A participatory-based discovery and rendezvous mobile marketing framework","volume":"32","author":"Ntalkos","year":"2015","journal-title":"Telemat. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lindgren, A., Doria, A., and Schelen, O. (2004). Probabilistic routing in intermittently connected networks. Service Assurance with Partial and Intermittent Resources, Springer.","DOI":"10.1007\/978-3-540-27767-5_24"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Haddadi, H., Hui, P., and Brown, I. (2010, January 20\u201324). MobiAd: Private and scalable mobile advertising. Proceedings of the Fifth ACM international Workshop on Mobility in the Evolving Internet Architecture, Chicago, IL, USA.","DOI":"10.1145\/1859983.1859993"},{"key":"ref_19","unstructured":"Lazer, W. (1994). Handbook of Demographics for Marketing & Advertising: New Trends in The American Marketplace, Lexington Books."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/9\/2\/16\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:34:22Z","timestamp":1760207662000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/9\/2\/16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5,1]]},"references-count":19,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["fi9020016"],"URL":"https:\/\/doi.org\/10.3390\/fi9020016","relation":{},"ISSN":["1999-5903"],"issn-type":[{"type":"electronic","value":"1999-5903"}],"subject":[],"published":{"date-parts":[[2017,5,1]]}}}