Paper 2021/1683

PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics

Valerie Fetzer, Marcel Keller, Sven Maier, Markus Raiber, Andy Rupp, and Rebecca Schwerdt


In this paper we propose Privacy-preserving User-data Bookkeeping & Analytics (PUBA), a building block destined to enable the implementation of business models (e.g., targeted advertising) and regulations (e.g., fraud detection) requiring user-data analysis in a privacy-preserving way. In PUBA, users keep an unlinkable but authenticated cryptographic logbook containing their historic data on their device. This logbook can only be updated by the operator while its content is not revealed. Users can take part in a privacy-preserving analytics computation, where it is ensured that their logbook is up-to-date and authentic while the potentially secret analytics function is verified to be privacy-friendly. Taking constrained devices into account, users may also outsource analytic computations (to a potentially malicious proxy not colluding with the operator). We model our novel building block in the Universal Composability framework and provide a practical protocol instantiation. To demonstrate the flexibility of PUBA, we sketch instantiations of privacy-preserving fraud detection and targeted advertising, although it could be used in many more scenarios, e.g. data analytics for multi-modal transportation systems. We implemented our bookkeeping protocols and an exemplary outsourced analytics computation based on logistic regression using the MP-SPDZ MPC framework. Performance evaluations using a smartphone as user device and more powerful hardware for operator and proxy suggest that PUBA for smaller logbooks can indeed be practical.

Available format(s)
Cryptographic protocols
Publication info
Published elsewhere. Proceedings on Privacy Enhancing Technologies
Contact author(s)
andy rupp @ uni lu
svmaier @ ira uni-karlsruhe de
marcel keller @ data61 csiro au
valerie fetzer @ kit edu
markus raiber @ kit edu
rebecca schwerdt @ kit edu
2021-12-22: received
Short URL
Creative Commons Attribution


      author = {Valerie Fetzer and Marcel Keller and Sven Maier and Markus Raiber and Andy Rupp and Rebecca Schwerdt},
      title = {PUBA: Privacy-Preserving User-Data Bookkeeping and Analytics},
      howpublished = {Cryptology ePrint Archive, Paper 2021/1683},
      year = {2021},
      note = {\url{}},
      url = {}
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