Research Article | Open Access | Download PDF
Volume 7 | Issue 3 | Year 2017 | Article Id. IJPTT-V32P403 | DOI : https://doi.org/10.14445/22492615/IJPTT-V32P403A Conceptual Framework for Mobile-Ad Management using Caching and Relevance Mapping with Privacy Protection
S.Balaji, M.Charumathi, M.Hindu, G.Navaneetha
Citation :
S.Balaji, M.Charumathi, M.Hindu, G.Navaneetha, "A Conceptual Framework for Mobile-Ad Management using Caching and Relevance Mapping with Privacy Protection," International Journal of P2P Network Trends and Technology (IJPTT), vol. 7, no. 3, pp. 10-14, 2017. Crossref, https://doi.org/10.14445/22492615/IJPTT-V32P403
Abstract
Mobile advertisements in smart phones and gadgets have increased. But the privacy of mobile users is under annoyance. The proposed system is to aggregate user’s interests when requesting advertisements to hide user identities from the ad server. The main adversary in our model is the server distributing the ads, which is trying to identify users and track them, and to a lesser extent, other peers in the wireless network. When a node is interested in an ad, it forms a group of nearby nodes seeking ads and willing to cooperate to achieve privacy. Peer sends the advertisement request to server through primary peer and random choosing peer. Peer who is selected as a random peer will encrypt the advertisement using public key and forward to primary peer, then primary peer verifies the signature and then re-encrypts the advertisement request. The relevance mapping is done in the ad-server and associated as requests are aggregated. Another mechanism is proposed to implement the billing process without disclosing user identities using piggybacking.
Keywords
peer, primary peer, content provider, service provider, piggybacking.
References
[1] Yuqing Sun and Guangjun Ji, “Privacy Preserving in
Personalized Mobile Marketing,” in proceeding of the 6th International
conference, AMT 2010, Toronto.
[2] Hamed Haddadi, Pan Hui, Ian Brown, “MobiAd:
Private and Scalable Mobile Advertising,” in proc. 5th ACM International
workshop on Mobility in the Evolving Internet Architecture, 2010, USA.
[3] Ahmed Fawaz, Ali Hojaij, Hadi Kobeissi, “PrivAd: A
Privacy Preserving Targeted Mobile Advertising Architecture,” thesis submitted
in the American University of Brirut 2011.
[4] Hamed Haddadi, Pan Hui, Tristan Henderson and Ian Brown,
“Targeted Advertising on the Handset: Privacy and Security Challenges,” in HCI
Doctoral Consortium, New castle, 2011, UK.
[5] Shashi Shekhar, Michael Dietz, Dan S. Wallach, “AdSplit:
Separating smartphone advertising from applications,” in proc. of the 21st
USENIX conference on Security symposium 2012.
[6] Saikat Guha, Bin Cheng, Paul Francis, “Privad: Practical
Privacy in Online Advertising,” in proc. of the 8th USENIX conference on
Networked systems design and implementation 2011.
[7] Theodore Book, Dan S. Wallach, “An Empirical Study of
Mobile Ad Targeting,” arXiv: 1502.06577v1 [cs.CR] 23 Feb 2015.
[8] Wei Wang, Linlin Yang, Yanjiao Chen, Qian Zhang, “A
privacy-aware framework for targeted advertising,” in the International Journal
of Computer and Telecommunications Networking 2015.
[9] Suman Nath, “MAdScope: Characterizing Mobile In-App
Targeted Ads,” in Proceedings of the 13th Annual International Conference on
Mobile Systems, Applications, and Services 2015.
[10] Wei Meng, Ren Ding, Simon P. Chung, Steven Han, and
Wenke Lee, “The Price of Free: Privacy Leakage in Personalized Mobile In-App
Ads” in proc. of the 23rd Annual Network and Distributed System Security
Symposium (NDSS), February 2016.
[11] Amit Datta, Michael Carl Tschantz, and Anupam Datta,
“Automated Experiments on Ad Privacy Settings,” in proc. on Privacy Enhancing
Technologies, 2015.
[12] Michael Backes, Aniket Kate, Matteo Maffei, and Kim
Pecina, “ObliviAd: Provably Secure and Practical Online Behavioral
Advertising,” in proc. of the 2012 IEEE Symposium on Security and Privacy 2012.
[13] Ting Ning, Zhipeng Yang, Hongyi Wu, and Zhu Han, “Self- Interest-Driven Incentives for Ad Dissemination in Autonomous Mobile Social Networks,” in proc. IEEE, 2013.
[14] Paul Barford,
Igor Canadi, Darja Krushevskaja, Qiang Ma, S. Muthukrishnan, “Adscape:
Harvesting and Analyzing Online Display Ads,” in proc. of the 23rd
international conference on World Wide Web 2014.
[15] Bin Liu, Anmol Sheth, Udi Weinsberg, Jaideep
Chandrashekar, Ramesh Govindan, “AdReveal: Improving Transparency Into Online
Targeted,” in proc. of the twelfth ACM workshop on Hot Topics in Networks, 2013.