Muhammad Haris Mughees

Muhammad Haris Mughees, Gonçalo Pestana, Alex Davidson et al.

In order to preserve the possibility of an Internet that is free at the point of use, attention is turning to new solutions that would allow targeted advertisement delivery based on behavioral information such as user preferences, without compromising user privacy. Recently, explorations in devising such systems either take approaches that rely on semantic guarantees like $k$-anonymity -- which can be easily subverted when combining with alternative information, and do not take into account the possibility that even knowledge of such clusters is privacy-invasive in themselves. Other approaches provide full privacy by moving all data and processing logic to clients -- but which is prohibitively expensive for both clients and servers. In this work, we devise a new framework called PrivateFetch for building practical ad-delivery pipelines that rely on cryptographic hardness and best-case privacy, rather than syntactic privacy guarantees or reliance on real-world anonymization tools. PrivateFetch utilizes local computation of preferences followed by high-performance single-server private information retrieval (PIR) to ensure that clients can pre-fetch ad content from servers, without revealing any of their inherent characteristics to the content provider. When considering an database of $>1,000,000$ ads, we show that we can deliver $30$ ads to a client in 40 seconds, with total communication costs of 192KB. We also demonstrate the feasibility of PrivateFetch by showing that the monetary cost of running it is less than 1% of average ad revenue. As such, our system is capable of pre-fetching ads for clients based on behavioral and contextual user information, before displaying them during a typical browsing session. In addition, while we test PrivateFetch as a private ad-delivery, the generality of our approach means that it could also be used for other content types.

Muhammad Haris Mughees, Zhiyun Qian, Zubair Shafiq et al.

The rise of ad-blockers is viewed as an economic threat by online publishers, especially those who primarily rely on ad- vertising to support their services. To address this threat, publishers have started retaliating by employing ad-block detectors, which scout for ad-blocker users and react to them by restricting their content access and pushing them to whitelist the website or disabling ad-blockers altogether. The clash between ad-blockers and ad-block detectors has resulted in a new arms race on the web. In this paper, we present the first systematic measurement and analysis of ad-block detection on the web. We have designed and implemented a machine learning based tech- nique to automatically detect ad-block detection, and use it to study the deployment of ad-block detectors on Alexa top- 100K websites. The approach is promising with precision of 94.8% and recall of 93.1%. We characterize the spectrum of different strategies used by websites for ad-block detection. We find that most of publishers use fairly simple passive ap- proaches for ad-block detection. However, we also note that a few websites use third-party services, e.g. PageFair, for ad-block detection and response. The third-party services use active deception and other sophisticated tactics to de- tect ad-blockers. We also find that the third-party services can successfully circumvent ad-blockers and display ads on publisher websites.