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Tighter bounds on instance encoding invertibility reveal that deterministic encoders can be just as secure as their randomized counterparts, transforming our understanding of data privacy techniques.
Achieving up to 20X reductions in communication overhead and 10X latency cuts, DFA revolutionizes the efficiency of function secret sharing in privacy-preserving systems.
Choosing the right privacy-preserving ML technique is more than just latency: this benchmark reveals the hidden energy and monetary costs that can make MPC cheaper than FHE in some surprising scenarios.
Listwise preference optimization for diffusion models (Diffusion-LPO) beats pairwise DPO baselines, finally unlocking the potential of richer ranked human feedback.
