19,097 research outputs found
A Secure and Privacy-Preserving Targeted Ad-System
Thanks to its low product-promotion cost and its efficiency, targeted online advertising has become very popular. Unfortunately, being profile-based, online advertising methods violate consumers' privacy, which has engendered resistance to the ads. However, protecting privacy through anonymity seems to encourage click-fraud. In this paper, we define consumer's privacy and present a privacy-preserving, targeted ad system (PPOAd) which is resistant towards click fraud. Our scheme is structured to provide financial incentives to to all entities involved
Joint optimisation of privacy and cost of in-app mobile user profiling and targeted ads
Online mobile advertising ecosystems provide advertising and analytics
services that collect, aggregate, process and trade rich amount of consumer's
personal data and carries out interests-based ads targeting, which raised
serious privacy risks and growing trends of users feeling uncomfortable while
using internet services. In this paper, we address user's privacy concerns by
developing an optimal dynamic optimisation cost-effective framework for
preserving user privacy for profiling, ads-based inferencing, temporal apps
usage behavioral patterns and interest-based ads targeting. A major challenge
in solving this dynamic model is the lack of knowledge of time-varying updates
during profiling process. We formulate a mixed-integer optimisation problem and
develop an equivalent problem to show that proposed algorithm does not require
knowledge of time-varying updates in user behavior. Following, we develop an
online control algorithm to solve equivalent problem using Lyapunov
optimisation and to overcome difficulty of solving nonlinear programming by
decomposing it into various cases and achieve trade-off between user privacy,
cost and targeted ads. We carry out extensive experimentations and demonstrate
proposed framework's applicability by implementing its critical components
using POC `System App'. We compare proposed framework with other privacy
protecting approaches and investigate that it achieves better privacy and
functionality for various performance parameters
Privacy-preserving targeted advertising scheme for IPTV using the cloud
In this paper, 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 viewers/subscribers, a content provider (IPTV), an advertiser, and a cloud server. To provide high quality directed advertising service, 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 published on the cloud server periodically (e.g. weekly) 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 cloud, are considered (trade) secrets and therefore are protected as well. The cloud is oblivious to the published data, the queries of the advertiser as well as its own responses to these queries. Only a legitimate advertiser, endorsed with a so-called {\em trapdoor} by the IPTV, can query the cloud and utilize the query results. The performance of the proposed scheme is evaluated with experiments, which show that the scheme is suitable for practical usage
Online advertising: analysis of privacy threats and protection approaches
Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft
Fighting Online Click-Fraud Using Bluff Ads
Online advertising is currently the greatest source of revenue for many
Internet giants. The increased number of specialized websites and modern
profiling techniques, have all contributed to an explosion of the income of ad
brokers from online advertising. The single biggest threat to this growth, is
however, click-fraud. Trained botnets and even individuals are hired by
click-fraud specialists in order to maximize the revenue of certain users from
the ads they publish on their websites, or to launch an attack between
competing businesses.
In this note we wish to raise the awareness of the networking research
community on potential research areas within this emerging field. As an example
strategy, we present Bluff ads; a class of ads that join forces in order to
increase the effort level for click-fraud spammers. Bluff ads are either
targeted ads, with irrelevant display text, or highly relevant display text,
with irrelevant targeting information. They act as a litmus test for the
legitimacy of the individual clicking on the ads. Together with standard
threshold-based methods, fake ads help to decrease click-fraud levels.Comment: Draf
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