Privacy in Targeted Advertising: A Survey
September 15, 2020 ยท The Cartographer ยท ๐ arXiv.org
"No code URL or promise found in abstract"
"Title-pattern auto-detect: Privacy in Targeted Advertising: A Survey"
Evidence collected by the PWNC Scanner
Authors
Imdad Ullah, Roksana Boreli, Salil S. Kanhere
arXiv ID
2009.06861
Category
cs.CR: Cryptography & Security
Citations
22
Venue
arXiv.org
Last Checked
2 days ago
Abstract
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 user's personal data, which has prompted serious privacy concerns among both individuals and organisations. This article presents a detailed survey of the associated privacy risks and proposed solutions in a mobile environment. We outline details of the information flow between the advertising platform and ad/analytics networks, the profiling process, advertising sources and criteria, 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 user 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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
๐ป
Ghosted
How To Backdoor Federated Learning
R.I.P.
๐ป
Ghosted