GANGES

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Motivation

Ensuring anonymity guarantees is crucial for the implementation of new, data-based applications. Although traditional anonymization and pseudonymization methods such as k-anonymization or differential privacy are already used in practice, they have proven to be unsuitable for real-world applications. The reasons for this include the fact that in real applications, instead of simple numerical data types, more complex data types are playing an increasingly important role, such as series of location data or hierarchical data structures that map physical or logical conditions. In addition, existing methods cannot support the communication and architecture models relevant to corporate environments, meaning that they cannot be integrated into established software stacks. This applies in particular to stream processing models, in which large volumes of data continuously pass through a processing chain without being permanently stored. There is also a lack of established metrics and procedures for estimating and balancing the processing effort and loss of information associated with different anonymization methods.

Objectives and approach

The aim of the project "Ensuring anonymity guarantees in enterprise streaming applications" (GANGES) is to reduce the aforementioned obstacles by developing practice-oriented anonymization procedures that are based on real needs with anonymity guarantees. To this end, new methods for stream anonymization are being developed on the basis of specific use cases from the energy and building industry as well as existing basic methods. Particularly, real-world architectures and their distributed nature shall be considered for the development of new approaches. This also includes modular processing strategies that can enhance performance and efficiency as well as leveraging the advantage of distributed systems. Complex data types that are used in practice and pose a particular anonymization challenge are taken into account. The developed methods are integrated into established open-source streaming frameworks with minimal effort. Furthermore, new approaches for the assessment and experimental determination of effort and the remaining utility value of the data after anonymization are developed. Based on this, the procedures and integration technologies are evaluated and adapted during development.

Ensuring anonymity guarantees in enterprise streaming applications (GANGES)

During the first project phase, until the end of 2025, we examined how the reuse of data already collected in the fields of energy, building, and neighborhood management can generate economic and social value. The focus was particularly on developing technical solutions for anonymizing continuous data streams, taking into account business conditions, and integrating existing and new anonymization methods in a practical manner.

Modular anonymization for distributed data pipelines (GANGES 2)

In the second project phase, the focus remains on the anonymization of continuous data streams. In particular, the project will investigate how the anonymization methods developed and their extensions can be integrated into practical system architectures and data pipelines. It is particularly important that the methods can be integrated into distributed and dynamic systems and that the characteristics of the respective system components are taken into account.

Innovations and perspectives

The GANGES project makes valuable contributions to the concrete practical use of advanced anonymization methods and models in real application contexts, both on a theoretical and application-related level. The resulting new possibilities for data processing open up a wide range of economic and social potential. They are legally compliant and also protect economic interests across application and domain boundaries. In the area of streaming data in particular, this opens up a wide range of new possibilities for use and exploitation that could not be implemented in a legally compliant manner without adequate anonymization. For example, the project results are to be incorporated into the further development of a so-called SaaS solution (Software as a Service), which is provided via the internet or the cloud for decentralized energy management. This will enable improved forecasts and incentives for user behavior in electromobility. In scientific and technical terms, significant, paradigmatically new contributions can also be expected in the field of privacy-enhancing technologies and privacy engineering.

Publications

C. Brunn and F. Tschorsch, “DeZent: decentralized z-anonymity with privacy-preserving coordination,”  2026. [Link]

C Brunn, S Rehms, F Tschorsch, "GANGES: Gewährleistung von Anonymitäts-Garantien in Enterprise-Streaminganwendungen; Teilvorhaben: Neue Verfahren (GANGES-NeVa)."  Sachbericht zum Teilvorhaben im Verbundprojekt. Technische Informationsbibliothek 2026. [Link]

S. Rehms, S. Köpsell, V. Klös, and F. Tschorsch, “Information inference diagrams: Complementing privacy and security analyses beyond data flows,” in Privacy Technologies and Policy - 13th Annual Privacy Forum, APF 2025, Frankfurt am Main, Germany, October 22-23, 2025, Proceedings, ser. Lecture Notes in Computer Science, vol. 16183. Springer, 2025, pp. 202–220. [Link]

S. N. von Voigt, L. Mehner, and F. Tschorsch, “From theory to comprehension: A comparative study of differential privacy and k-anonymity,” in Proceedings of the Fourteenth ACM Conference on Data and Application Security and Privacy, CODASPY 2024, Porto, Portugal, June 19-21, 2024. ACM, 2024, pp. 221–232. [Link]

J. Allmann, S. N. von Voigt, and F. Tschorsch, “An applied perspective: Estimating the differential identifiability risk of an exemplary SOEP data set,” in IEEE European Symposium on Security and Privacy Workshops, EuroS&PW 2024, Vienna, Austria, July 8-12, 2024. IEEE, 2024, pp. 48–55. [Link]

C. Brunn, S. N. n. von Voigt, and F. Tschorsch, “Analyzing continuous ks-anonymization for smart meter data,” in Computer Security. ESORICS 2023 International Workshops. LNCS, vol. 14398, 2023, p. 272–282. [Link]

Acknowledgements

© Nein

The GANGES2 project is funded by the Federal Ministry of Research, Technology and Space (BMFTR).