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Transparency is one of the most important principles of modern privacy regulations, such as the GDPR or CCPA. To be compliant with such regulatory frameworks, data controllers must provide data subjects with precise information about the collection, processing, storage, and transfer of personal data. To do so, respective facts and details must be compiled and always kept up to date. In traditional, rather static system environments, this inventory (including details such as the purposes of processing or the storage duration for each system component) could be done manually. In current circumstances of agile, DevOps-driven, and cloud-native information systems engineering, however, such manual practices do not suit anymore, making it increasingly hard for data controllers to achieve regulatory compliance. To allow for proper collection and maintenance of always up-to-date transparency information smoothly integrating into DevOps practices, we herein propose a set of novel approaches explicitly tailored to specific phases of the DevOps lifecycle most relevant in matters of privacy-related transparency and accountability at runtime: Release, Operation, and Monitoring. For each of these phases, we examine the specific challenges arising in determining the details of personal data processing, develop a distinct approach and provide respective proof of concept implementations that can easily be applied in cloud native systems. We also demonstrate how these components can be integrated with each other to establish transparency information comprising design- and runtime-elements. Furthermore, our experimental evaluation indicates reasonable overheads. On this basis, data controllers can fulfill their regulatory transparency obligations in line with actual engineering practices., Comment: preprint, accepted for the 16th IEEE International Conference on Cloud Computing 2023, IEEE Cloud 2023

Transparency regarding the processing of personal data in online services is a necessary precondition for informed decisions on whether or not to share personal data. In this paper, we argue that privacy interfaces shall incorporate the context of display, personal preferences, and individual competences of data subjects following the principles of universal design and usable privacy. Doing so requires -- among others -- to consciously decouple the provision of transparency information from their ultimate presentation. To this end, we provide a general model of how transparency information can be provided from a data controller to data subjects, effectively leveraging machine-readable transparency information and facilitating versatile presentation interfaces. We contribute two actual implementations of said model: 1) a GDPR-aligned privacy dashboard and 2) a chatbot and virtual voice assistant enabled by conversational AI. We evaluate our model and implementations with a user study and find that these approaches provide effective and time-efficient transparency. Consequently, we illustrate how transparency can be enhanced using machine-readable transparency information and how data controllers can meet respective regulatory obligations., Comment: Preprint, accepted to the Privacy Symposium 2023 https://privacysymposium.org/

Machine-readable representations of privacy policies are door openers for a broad variety of novel privacy-enhancing and, in particular, transparency-enhancing technologies (TETs). In order to generate such representations, transparency information needs to be extracted from written privacy policies. However, respective manual annotation and extraction processes are laborious and require expert knowledge. Approaches for fully automated annotation, in turn, have so far not succeeded due to overly high error rates in the specific domain of privacy policies. In the end, a lack of properly annotated privacy policies and respective machine-readable representations persists and enduringly hinders the development and establishment of novel technical approaches fostering policy perception and data subject informedness. In this work, we present a prototype system for a `Human-in-the-Loop' approach to privacy policy annotation that integrates ML-generated suggestions and ultimately human annotation decisions. We propose an ML-based suggestion system specifically tailored to the constraint of data scarcity prevalent in the domain of privacy policy annotation. On this basis, we provide meaningful predictions to users thereby streamlining the annotation process. Additionally, we also evaluate our approach through a prototypical implementation to show that our ML-based extraction approach provides superior performance over other recently used extraction models for legal documents., Comment: Accepted for 2023 IEEE European Symposium on Security and Privacy Workshops (EuroS&P)

Federated learning may be subject to both global aggregation attacks and distributed poisoning attacks. Blockchain technology along with incentive and penalty mechanisms have been suggested to counter these. In this paper, we explore verifiable off-chain computations using zero-knowledge proofs as an alternative to incentive and penalty mechanisms in blockchain-based federated learning. In our solution, learning nodes, in addition to their computational duties, act as off-chain provers submitting proofs to attest computational correctness of parameters that can be verified on the blockchain. We demonstrate and evaluate our solution through a health monitoring use case and proof-of-concept implementation leveraging the ZoKrates language and tools for smart contract-based on-chain model management. Our research introduces verifiability of correctness of learning processes, thus advancing blockchain-based federated learning.

The purpose of regulatory data minimization obligations is to limit personal data to the absolute minimum necessary for a given context. Beyond the initial data collection, storage, and processing, data minimization is also required for subsequent data releases, as it is the case when data are provided using query-capable Web APIs. Data-providing Web APIs, however, typically lack sophisticated data minimization features, leaving the task open to manual and all too often missing implementations. In this paper, we address the problem of data minimization for data-providing, query-capable Web APIs. Based on a careful analysis of functional and non-functional requirements, we introduce Janus, an easy-to-use, highly configurable solution for implementing legally compliant data minimization in GraphQL Web APIs. Janus provides a rich set of information reduction functionalities that can be configured for different client roles accessing the API. We present a technical proof-of-concept along with experimental measurements that indicate reasonable overheads. Janus is thus a practical solution for implementing GraphQL APIs in line with the regulatory principle of data minimization., Comment: Preprint version (2022-03-18) This version of the contribution has been accepted for publication at the 22nd International Conference on Web Engineering (ICWE 2022), Bari, Italy

Cloud native information systems engineering enables scalable and resilient service infrastructures for all major online offerings. These are built following agile development practices. At the same time, a growing demand for privacy-friendly services is articulated by societal norms and policy through effective legislative frameworks. In this paper, we identify the conceptual dimensions of cloud native privacy engineering and propose an integrative approach to be addressed in practice to overcome the shortcomings of existing privacy enhancing technologies. Furthermore, we propose a reference software development lifecycle called DevPrivOps to enhance established agile development methods with respect to privacy. Altogether, we show that cloud native privacy engineering advances the state of the art of privacy by design and by default using latest technologies., Comment: preprint version (2021-12-01), accepted for the Post-Proceedings at the 16th IFIP Summer School on Privacy and Identity Management 2021