Privacy-preserving targeted mobile advertising: A Blockchain-based framework for mobile ads

Ullah, Imdad; Kanhere, Salil S.; Boreli, Roksana

doi:10.48550/arxiv.2008.10479

Cited by 2 publications

(14 citation statements)

References 30 publications

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“…This can be performed by first capturing the traces from the apps that execute and download the ad traffic and then investigating the traffic characteristics. Characterising and inspecting the ad traffic can give information about the approaches used by multiple publishers, the various mechanisms used to deliver ads by the publishers, the use of different ad servers, and the ad networks themselves [24]. Similarly, it helps identify any analytics traffic used by the ad networks to target with relevant ads.…”

Section: Ads Traffic Identificationmentioning

confidence: 99%

“…As opposed to above implementation, we suggested another proposal [24] for ad presentations and clicks with the use of mining Cryptocurrency (e.g. Bitcoin).…”

Section: Implementations Of Private Billing For In-app Mobile Adverti...mentioning

confidence: 99%

“…Bitcoin). The major aim for this proposal was for preserving user privacy, secure payment and for compatibility with the underlying AdBlock proposal [24] for mobile advertising system over Blockchain. Following notations are used in this proposal: price tags C Ad ID prs and C Ad ID clk for ad presentation and click; various wallets i.e.…”

Section: Implementations Of Private Billing For In-app Mobile Adverti...mentioning

confidence: 99%

“…In particular, Privad [140] and Adnostic [26] use the approach of downloading a wide range of ads from the ad network and locally (in the browser or on the mobile device) selecting ads that match the user's profile. On the other hand, there are a smaller number of works address privacy for mobile in-app advertising, with representative works e.g., [7], [8], [24], [28], [34], [149], [150], suggest the app-based user profiling, stored locally on mobile device. The [7] is based on various mechanisms of PIR and it complements the existing advertising system and is conceptually closest to [149], which uses Oblivious RAM (ORAM) to perform Private Information Retrieval (PIR) on a secure coprocessor hardware.…”

Section: Private Ad Ecosystemsmentioning

confidence: 99%

“…However, we note that this framework does not include all the components of an advertising system including user profiles construction, detailed structure of various Blockchain-based transactions and operations, or other entities such as Miner and the billing process. Our recent work, AdBlock [24], presents a detailed framework (in addition to Android-based POC implementation i.e. a Bespoke Miner) for privacy preserving user profiling, privately requesting ads, the billing mechanisms for presented and clicked ads, mechanism for uploading ads to the cloud, various types of transactions to enable advertising operations in Blockchain-based network, and methods for access policy for accessing various resources, such as accessing ads, storing mobile user profiles etc.…”

Section: Blockchain-based Advertising Systemsmentioning

confidence: 99%

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Privacy in Targeted Advertising: A Survey

Ullah¹,

Boreli²,

Kanhere³

2020

Preprint

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Targeted advertising has transformed the marketing trend for any business by creating new opportunities for advertisers to reach prospective customers by delivering them personalised ads using an infrastructure of a variety of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process and trade a rich amount of user's personal data, which has prompted serious privacy concerns among individuals and organisations. This article presents a detailed survey of privacy risks including the information flow between advertising platform and ad/analytics networks, the profiling process, the advertising sources and criteria, the measurement analysis of targeted advertising based on user's interests and profiling context and ads delivery process in both in-app and in-browser targeted ads. We provide detailed discussion of challenges in preserving user privacy that includes privacy threats posed by the advertising and analytics companies, how private information is extracted and exchanged among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks, in addition to, overview data and tracking sharing technologies. Following, we present various techniques for preserving user privacy and a comprehensive analysis of various proposals founded on those techniques and compare them based on the underlying architectures, the privacy mechanisms and the deployment scenarios. Finally we discuss some potential research challenges and open research issues.

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Section: Ads Traffic Identificationmentioning

confidence: 99%

“…As opposed to above implementation, we suggested another proposal [24] for ad presentations and clicks with the use of mining Cryptocurrency (e.g. Bitcoin).…”

Section: Implementations Of Private Billing For In-app Mobile Adverti...mentioning

confidence: 99%

Section: Implementations Of Private Billing For In-app Mobile Adverti...mentioning

confidence: 99%

Section: Private Ad Ecosystemsmentioning

confidence: 99%

Section: Blockchain-based Advertising Systemsmentioning

confidence: 99%

See 3 more Smart Citations

Privacy in Targeted Advertising: A Survey

Ullah¹,

Boreli²,

Kanhere³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Joint Optimization of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

Ullah

Binbusayyis

2022

IEEE Access

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Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process, and trade a rich amount of consumers' personal data and carry out interests-based ad targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using internet services. In this paper, we address users' 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 ad targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during the profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that the proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve the equivalent problem and to overcome the difficulty of solving nonlinear programming by decomposing it into various cases and to achieve a trade-off between user privacy, cost, and targeted ads. We carry out extensive experimentations and demonstrate the proposed framework's applicability by implementing its critical components using POC (Proof Of Concept) 'System App'. We compare the proposed framework with other privacy protecting approaches and investigate whether it achieves better privacy and functionality for various performance parameters.

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Privacy-preserving targeted mobile advertising: A Blockchain-based framework for mobile ads

Cited by 2 publications

References 30 publications

Privacy in Targeted Advertising: A Survey

Privacy in Targeted Advertising: A Survey

Joint Optimization of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

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