Skip to content
Open access

Privacy-First Engineering: Re-Architecting High-Scale Real-Time Advertising Platforms for Global Data Regulations

Sep 2026 · East African Journal of Information Technology · 0 citations

TL;DR

A privacy-first re-architecture of high-scale RTB platforms is proposed, introducing a layered design comprising a consent management layer, a data minimisation pipeline, purpose-binding enforcement modules, and differential privacy mechanisms for aggregate analytics.

Abstract

Real-time bidding (RTB) and programmatic advertising ecosystems have been engineered over more than a decade primarily for throughput and revenue optimisation, with data privacy treated largely as a post-hoc compliance exercise. The enactment of the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA) in the United States, and Brazil's Lei Geral de Proteção de Dados (LGPD) has fundamentally disrupted this design philosophy. These regulations impose requirements for informed consent, data minimisation, purpose limitation, and cross-border transfer controls that are structurally incompatible with legacy advertising stack architectures. Empirical studies reveal the depth of the problem: only a small minority of cookie consent banners were found to be fully GDPR-compliant (Utz et al., 2019), and a majority of websites registered with the IAB Transparency and Consent Framework violated at least one regulatory requirement (Matte et al., 2020). Browser fingerprinting, a method that bypasses consent mechanisms by never storing a client-side identifier, has been shown to correctly re-identify users across varied browser environments in the large majority of cases (Laperdrix et al., 2020), illustrating why surface-level compliance measures leave a structural gap. This paper proposes a privacy-first re-architecture of high-scale RTB platforms, introducing a layered design comprising a consent management layer, a data minimisation pipeline, purpose-binding enforcement modules, and differential privacy mechanisms for aggregate analytics. Engineering trade-offs across latency, throughput, and multi-jurisdiction deployability are examined for the proposed architecture. Findings indicate that privacy-by-design principles, when integrated at the infrastructure level rather than the application layer, yield both regulatory compliance and competitive operational performance.

Read PDF

Similar papers

Sep 2026

Identity assurance at the last mile: A disbursement-time verification framework for cross-border compliance in the G20 payment reform era

Global cross-border payments reached approximately US$179tn in 2024, with business- to-business flows comprising the majority of that value. Despite this scale, a significant proportion of cross-border payments fail or are delayed by compliance issues, trapping substantial liquidity annually in the correspondent bankin...

Apurva Shrivastava · 0 citations

Doppio : Communication-Efficient and Secure Multi-Party Shuffle Differential Privacy

The augmented multi-party shuffle DP (AMP-SDP) model is proposed, which re-architects the data pipeline with a lightweight, versatile secret-shared intermediary layer that decentralizes trust while minimizing online communication costs and provides structural security hardening against both shuffler compromise and user-sid...

Wentao Dong, Yang Cao, Cong Wang et al. · 0 citations
Open access Aug 2026

When less data is better: Exploratory privacy-by-design in AI-based human resource analytics based on synthetic data

The use of Artificial Intelligence (AI) in Human Resource (HR)-related processes raises ethical and legal concerns, within the European context governed by the GDPR. This study presents a proof-of-concept computational investigation proposing an approach to securing HR data by using Large Language Models-LLMs (such as:...

Cristina Iancu, S. Oprea, A. Bâra et al. · 0 citations
Preprint Sep 2026

Proof-of-Retention: A Framework for Auditable Cross-Organization Data Sharing

The rapid adoption of AI across industries for (e.g.) fine-tuning and analytics has accelerated the need for high-quality data. To satisfy this demand, public and private entities will buy and sell data with other organizations. But such data sharing can and does violate privacy norms and laws. EU and American lawmaker...

Kyle MacMillan, Sanjay Krishnan · 0 citations
#software testing Preprint Sep 2026

5G-Shark: A Network Security Auditor for 5G Subscriber Privacy and Unauthenticated Signalling Resilience

5G-Shark is presented, a security assessment tool and methodology that turns a legitimate mobility procedure against the subscriber, and empirical evidence that in several commercial deployments, temporary identifiers are re-allocated in near-sequential steps that keep successive values linkable, a weakness that enable...

Oscar Lasierra, Gines Garcia-Aviles, A. Skarmeta et al. · 0 citations
Book Open access Oct 2026

Exploring The Missing Half: A Dual-Perspective Measurement of Encrypted Recursive-to-Authoritative DNS

RFC 9539 defines a unilateral opportunistic mechanism for deploying encrypted DNS on the recursive-to-authoritative link, yet its real-world deployment and operational behavior remain incompletely characterized. Mapping this ecosystem requires complementary observations of both resolver behavior and authoritative capab...

Yu-Jia Zhu, Lin-Kang Zhang, Bai-Yang Li et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.