Enabling Privacy Preserving Mobile Advertising via Private Information Retrieval

Ullah, Imdad; Sarwar, Babil Golam; Boreli, Roksana; Kanhere, Salil S.; Katzenbeisser, Stefan; Hollick, Matthias

doi:10.1109/lcn.2017.63

Cited by 14 publications

(44 citation statements)

References 20 publications

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“…The ad contains the landing page (web address of an ad-URL) and JavaScript code that is executed where some of the static objects are downloaded (such as a PNG, (Step 3)). Two actions are performed after clicking an ad: a Conversion cookie 7 is set inside phone (Step 4) and the web server associated with the ad is contacted. The landing page may contain other list of servers (mainly residing in Content Delivery Networks) where some of the static objects are downloaded and a complete HTML page is shown to the user (Step 5).…”

Section: Ad Delivery Processmentioning

confidence: 99%

“…In the process of data monetisation, the ads/analytics companies aggressively look for all the possible ways to gather personal data from the users, including purchasing users' personal data from third parties. This poses serious threats to privacy of users [7], [8], [9], [10], [11], [12], when apps indicating sensitive information, e.g., a gaming app showing a gambling problem, are the basis for profiling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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.<br>

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Section: Ad Delivery Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Privacy in targeted advertising: A survey

Ullah¹,

Boreli²,

Kanhere³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The advertising companies enable user tracking within apps, and hence profiling users, where user's personal information plays an important role in ads targeting. However, significant privacy concerns are associated with it, as suggested in a number of studies [1], [2], [3], [4], [5], [6]. Other studies [7], [8] indicate, unless consumers have specifically consented to it, that they have a direct relationship between consumer attitude and their behavior, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…There are other works on protecting user's privacy by obfuscating user profiles 5 [5], [15] and based on security techniques [2], enabling user privacy [16], [17] by suggesting design changes to Android location API based on laboratory study [18] to understand developer's behavior. Other privacy preserving advertising systems use a mix of cryptography techniques and obfuscation mechanisms, e.g., [19], [20] and targeted mobile coupon delivery scheme using Blockchain [21].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the decryption time evaluates to (0.001sec, 0.005mse) and (0.35sec, 1.67sec) with Avg and St.Dev of (0.002sec, 0.0009sec) and (0.8sec, and 0.36sec). Based on our observations [2], [5], we note that this is an (equivalent) acceptable amount of processing time to that of when a mobile app requests ad in the beginning of an app launch or during the app usage session.…”

Section: Introductionmentioning

confidence: 99%

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

Ullah¹

2020

Preprint

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The targeted advertising is based on preference profiles inferred via relationships among individuals, their monitored responses to previous advertising and temporal activity over the Internet, which has raised critical privacy concerns. In this paper, we present a novel proposal for a Blockchain-based advertising platform that provides: a system for privacy preserving user profiling, privately requesting ads from the advertising system, the billing mechanisms for presented and clicked ads, the advertising system that uploads ads to the cloud according to profiling interests, various types of transactions to enable advertising operations in Blockchain-based network, and the method that allows a cloud system to privately compute the access policies for various resources (such as ads, mobile user profiles). Our main goal is to design a decentralized framework for targeted ads, which enables private delivery of ads to users whose behavioral profiles accurately match the presented ads, defined by the ad system. We implement a POC of our proposed framework i.e. a Bespoke Miner and experimentally evaluate various components of Blockchain-based in-app advertising system, implementing various critical components; such as, evaluating user profiles, implementing access policies, encryption and decryption of users' profiles. We observe that the processing delay for traversing policies of various tree sizes, the encryption/decryption time of user profiling with various key-sizes and user profiles of various interests evaluates to an acceptable amount of processing time as that of the currently implemented ad systems.<br><br>

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Privacy in targeted advertising on mobile devices: a survey

Ullah

Boreli²,

Kanhere

2022

Int. J. Inf. Secur.

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Targeted advertising has transformed the marketing landscape for a wide variety of businesses, by creating new opportunities for advertisers to reach prospective customers by delivering personalised ads, using an infrastructure of a number of intermediary entities and technologies. The advertising and analytics companies collect, aggregate, process, and trade a vast amount of users’ personal data, which has prompted serious privacy concerns among both individuals and organisations. This article presents a comprehensive survey of the privacy risks and proposed solutions for targeted advertising in a mobile environment. We outline details of the information flow between the advertising platform and ad/analytics networks, the profiling process, the measurement analysis of targeted advertising based on user’s interests and profiling context, and the ads delivery process, for both in-app and in-browser targeted ads; we also include an overview of data sharing and tracking technologies. We discuss challenges in preserving the mobile user’s privacy that include threats related to private information extraction and exchange among various advertising entities, privacy threats from third-party tracking, re-identification of private information and associated privacy risks. Subsequently, we present various techniques for preserving user privacy and a comprehensive analysis of the proposals based on such techniques; we compare the proposals based on the underlying architectures, privacy mechanisms, and deployment scenarios. Finally, we discuss the potential research challenges and open research issues.

show abstract

Enabling Privacy Preserving Mobile Advertising via Private Information Retrieval

Cited by 14 publications

References 20 publications

Privacy in targeted advertising: A survey

Privacy in targeted advertising: A survey

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

Privacy in targeted advertising on mobile devices: a survey

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