Abstract 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.

Black-box accumulation (BBA) is a building block which enables a privacy-preserving implementation of point collection and redemption, a functionality required in a variety of user-centric applications including loyalty programs, incentive systems, and mobile payments. By definition, BBA+ schemes (Hartung et al. CCS ‘17) offer strong privacy and security guarantees, such as unlinkability of transactions and correctness of the balance flows of all (even malicious) users. Unfortunately, the instantiation of BBA+ presented at CCS ‘17 is, on modern smartphones, just fast enough for comfortable use. It is too slow for wearables, let alone smart-cards. Moreover, it lacks a crucial property: For the sake of efficiency, the user’s balance is presented in the clear when points are deducted. This may allow to track owners by just observing revealed balances, even though privacy is otherwise guaranteed. The authors intentionally forgo the use of costly range proofs, which would remedy this problem.