Gonçalo Pestana

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.

Gonçalo Pestana, Iñigo Querejeta-Azurmendi, Panagiotis Papadopoulos et al.

Online advertising fuels the (seemingly) free internet. However, although users can access most of the web services free of charge, they pay a heavy coston their privacy. They are forced to trust third parties and intermediaries, who not only collect behavioral data but also absorb great amounts of ad revenues. Consequently, more and more users opt out from advertising by resorting to ad blockers, thus costing publishers millions of dollars in lost ad revenues. Albeit there are various privacy-preserving advertising proposals (e.g.,Adnostic, Privad, Brave Ads) from both academia and industry, they all rely on centralized management that users have to blindly trust without being able to audit, while they also fail to guarantee the integrity of the per-formance analytics they provide to advertisers. In this paper, we design and deploy THEMIS, a novel, decentralized and privacy-by-design ad platform that requires zero trust by users. THEMIS (i) provides auditability to its participants, (ii) rewards users for viewing ads, and (iii) allows advertisers to verify the performance and billing reports of their ad campaigns. By leveraging smart contracts and zero-knowledge schemes, we implement a prototype of THEMIS and early performance evaluation results show that it can scale linearly on a multi sidechain setup while it supports more than 51M users on a single-sidechain.

Gonçalo Pestana, Iñigo Querejeta-Azurmendi, Panagiotis Papadopoulos et al.

Online advertising fuels the (seemingly) free internet. However, although users can access most websites free of charge, they need to pay a heavy cost on their privacy and blindly trust third parties and intermediaries that absorb great amounts of adrevenues and user data. This is one of the reasons users opt out from advertising by resorting ad blockers thatin turn cost publishers millions of dollars in lost adrevenues. Existing privacy-preserving advertising approaches(e.g., Adnostic, Privad, Brave Ads) from both industry and academia cannot guarantee the integrity of the performance analytics they provide to advertisers, while they also rely on centralized management that users have to trust without being able to audit. In this paper, we propose THEMIS, a novel privacy-by-design ad platform that is decentralized and requires zero trust from users. THEMIS (i) provides auditability to all participants, (ii) rewards users for viewing ads, and (iii) allows advertisers to verify the performance and billing reports of their ad campaigns. To demonstrate the feasibility and practicability of our approach, we implemented a prototype of THEMIS using a combination of smart contracts and zero-knowledge schemes. Performance evaluation results show that during adreward payouts, THEMIS can support more than 51M users on a single-sidechain setup or 153M users ona multi-sidechain setup, thus proving that THEMIS scales linearly.