Secure Decision Forest Evaluation
This work addresses privacy and security concerns in machine learning evaluations for scenarios involving sensitive data and valuable model parameters, representing an incremental improvement in cryptographic protocols for secure model inference.
The paper tackles the problem of securely evaluating decision forests while protecting sensitive client inputs and server model thresholds, proposing a protocol that ensures privacy and soundness against malicious clients with a constant number of online rounds.
Decision forests are classical models to efficiently make decision on complex inputs with multiple features. While the global structure of the trees or forests is public, sensitive information have to be protected during the evaluation of some client inputs with respect to some server model. Indeed, the comparison thresholds on the server side may have economical value while the client inputs might be critical personal data. In addition, soundness is also important for the receiver. In our case, we will consider the server to be interested in the outcome of the model evaluation so that the client should not be able to bias it. In this paper, we propose a new offline/online protocol between a client and a server with a constant number of rounds in the online phase, with both privacy and soundness against malicious clients. CCS Concepts: $\bullet$ Security and Privacy $\rightarrow$ Cryptography.