This work presents the first longitudinal, consent-aware measurement of the Privacy Sandbox's deployment across the Web, and monitors the usage of all major APIs in the months preceding their retirement.
Abstract
On October 17th, 2025, Google announced the retirement of most Privacy Sandbox APIs, concluding nearly five years of experimentation with its alternative to privacy-invasive data collection on the Web. Designed to balance privacy with advertising functionality and cross-site tracking, the initiative faced repeated redesigns and limited ecosystem support. In this work, we present the first longitudinal, consent-aware measurement of the Privacy Sandbox's deployment across the Web. Using a custom call listener and weekly crawls of the top-10,000 websites, we monitor the usage of all major APIs in the months preceding their retirement. Adoption had already stagnated well before Google's announcement: most APIs were used by only a handful of actors, whose activity declined steadily throughout our study. Even the APIs that Google plans to maintain show no sign of growth. The sole exception is Cookies Having Independent Partitioned State (CHIPS). Overall, the demise of the Privacy Sandbox leaves unresolved the challenge of enabling privacy-preserving interest-based advertising.
This work presents the first privacy- and security-oriented measurement of M365 third-party applications, and finds that only 1,069 of them expose both descriptions and permission sets, with significant inconsistencies in transparency across official distribution channels.
Vincenzo Longo, Alberto Verna, Nikhil Jha et al.· 0 citations
This work filters app reviews for privacy- and security-related content, yielding a comprehensive corpus of over 600K reviews, and introduces TaxoScale, a pipeline that handles taxonomy construction at this scale by extending an expert-defined taxonomy via Recursive Hierarchical Clustering and LLM-based node naming.
Moghis Fereidouni, Vinaik Chhetri, Umar Farooq et al.· 0 citations
TeleGapper, a black-box dynamic analysis framework, is presented, a black-box dynamic analysis framework to assess the privacy posture of Mini Apps by capturing runtime network traffic, identifying third-party communications, and comparing observed data flows against disclosed privacy information.
Large Language Models (LLMs) are rapidly moving from research settings into the wild, deployed on enterprise infrastructure, personal devices, and edge platforms. While cloud deployments offer scalable compute, concerns over data sovereignty, compliance, latency, and third-party dependence are driving organizations toward edge and on-premise LLMs. This shift introduces new security and privacy challenges: limited compute and memory force aggressive optimizations, including quantization, pruning, model partitioning, and parameter-efficient adaptation, each of which can introduce vulnerabilities and reshape the threat landscape. We describe this tension as the Security-Efficiency Paradox, mechanisms that improve efficiency may weaken robustness, expose new attack surfaces, or increase privacy risks. We examine how compression can degrade safety alignment, how partitioned inference enables reconstruction attacks, and how continuous local adaptation may cause privacy leakage and model drift. To analyze these risks, we introduce a deployment-centric taxonomy organized around three architectural constraints: the Memory Wall, the Quadratic Wall, and the Compute Wall. We derive a unified constraint model that quantifies when unsafe optimizations become unavoidable, linking each wall to specific attack surfaces. Building on this model, we propose the Secure Operational Efficiency Score (SOES), a holistic metric balancing task accuracy, jailbreak resistance, and privacy against energy, memory, and latency, enabling practitioners to configure edge LLMs under real-world hardware limits. We further present a practical decision procedure and targeted mitigations for each optimization-induced vulnerability. Together, these contributions provide a co-designed framework for jointly evaluating security, privacy, and efficiency, laying a foundation for securing edge-native intelligent systems.
Ren-Yi Huang, Mingchen Li, Dumindu Samaraweera et al.· 0 citations
One of the core privacy features of smartphone operating systems is a permission framework that requires explicit user consent before granting apps access to private data. Such systems are deeply integrated into Google's Android and Apple's iOS, which together account for the majority of the smartphone operating system market. While permission systems can be seen as milestones in user empowerment and privacy protection, they offer users only a binary choice: whether an app can access a specific resource or not. As soon as an app is allowed to read a resource, the operating system loses control over its further use. Most apps have Internet access and can send permission-protected data, like a user's location, over the Internet, which can harm user privacy. To solve this problem, we present an addition to current permission systems that splits apps into multiple sandboxed processes to enforce fine-grained privacy and data-flow controls on smartphones. By default, our design forces apps to process permission-protected data locally on the device, thereby eliminating the need for apps to request runtime permissions for local-only use cases. We implement a proof-of-concept based on the Android Open Source Project code base. We showcase our framework's practicability by adapting multiple app use cases to our system, benchmarking its computational overhead, and discussing the implications for platform operators, developers, and users.
Lucas Becker, David Breuer, Matthias Hollick· Proceedings on Privacy Enhan...· 0 citations
The systematic and excessive data collection practices of tech companies have rendered online privacy both a necessity and a sought-after commodity. However, while the privacy risks of the web, mobile, and IoT ecosystems have been extensively examined, desktop environments have been largely overlooked. As desktop apps continue to be widely used, they remain a critical yet understudied dimension of user privacy. In this paper, we address this gap by presenting the first, to our knowledge, comprehensive study of the mechanisms designed to regulate and disclose data collection and sharing practices in the macOS ecosystem. We adopt an app-development-centric view, and shed light on the interactions between the various macOS mechanisms that mediate apps'data access. Driven by our findings, we develop NutriScan, an analysis framework that incorporates both static and dynamic analysis techniques to create a consolidated view of macOS apps'data practices and disclosures. We use our system to dynamically analyze 1K macOS apps, and find that 85% of them access user-data APIs without disclosing it. 49.7% also exfiltrate data to advertising entities and hosting providers, 12.5% of which do so without a corresponding disclosure. We find that desktop apps are being leveraged by online trackers to enrich user profiles and device fingerprints, thus shedding new light on the true scope of the online tracking ecosystem. Our analysis reveals how the macOS app ecosystem is comprised of disjoint mechanisms with divergent data abstractions, thus increasing complexity for developers while also facilitating undisclosed privacy-invasive practices. Accordingly, we propose a series of mitigations that aim to both streamline the data disclosure process for developers and improve Apple's app vetting process.
Jyotirmay Chauhan, Kostas Solomos, Mir Masood Ali et al.· 0 citations
