PACT: Enhancing Privacy and Efficiency in Tree Evaluation via Secure Parallel Comparison and Oblivious Tree Aggregation

This research paper presents PACT, a new method for evaluating decision tree models (algorithms that make predictions by asking yes/no questions in a sequence) while protecting privacy. PACT uses additive homomorphic encryption (a type of math that lets computers do calculations on secret, scrambled data without unscrambling it first) to keep both the tree model and the user's data private, while running faster than previous privacy-preserving approaches.