A practical privacy-preserving targeted advertising scheme for IPTV users
Khayati, Leyli Javid and Örencik, Cengiz and Savaş, Erkay and Ustaoğlu, Berkant A practical privacy-preserving targeted advertising scheme for IPTV users. International Journal of Information Security . ISSN 1615-5262 (Print) 1615-5270 (Online) Published Online First http://dx.doi.org/10.1007/s10207-015-0296-7
Official URL: http://dx.doi.org/10.1007/s10207-015-0296-7
In this work, we present a privacy-preserving scheme for targeted advertising via the Internet Protocol TV (IPTV). The scheme uses a communication model involving a collection of subscribers, a content provider (IPTV), advertisers and a semi-trusted server. To target potential customers, the advertiser can utilize not only demographic information of subscribers, but also their watching habits. The latter includes watching history, preferences for IPTV content and watching rate, which are periodically (e.g., weekly) published on a semi-trusted server (e.g., cloud server) along with anonymized demographics. Since the published data may leak sensitive information about subscribers, it is safeguarded using cryptographic techniques in addition to the anonymization of demographics. The techniques used by the advertiser, which can be manifested in its queries to the server, are considered (trade) secrets and therefore are protected as well. The server is oblivious to the published data and the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with so-called trapdoors by the IPTV, can query the cloud server and access the query results. Even when some background information about users is available, query responses do not leak sensitive information about the IPTV users. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is practical. The algorithms demonstrate both weak and strong scaling property and take advantage of high level of parallelism. The scheme can also be applied as a recommendation system.
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