The rapid growth of AI has significantly increased data center energy demand, placing increasing pressure on power grids. Demand Response (DR) programs utilize the flexibility of power consumers, such as data centers, to help balance the supply and demand in power grids. In collaborative data center DR participation, multiple data centers share information with an external coordinator for improved power dispatch and quality of service for their workloads. However, disclosing sensitive information such as power usage and workload performance to an untrusted third party raises significant privacy concerns. To address this, we use Multi-Party Computation (MPC) to perform secure power dispatch without revealing sensitive inputs. Standard MPC has heavy communication overhead, which challenges real-time DR requirements. To meet real-time DR requirements, we optimize our system by tailoring fixed-point bitwidths and substituting expensive divisions with polynomial approximations and Newton-Raphson iterations. Evaluated using the MP-SPDZ library, our optimizations yield up to 33 $$\times $$ fewer communication rounds and a 45 $$\times $$ speedup, delivering sub-second latency for up to 16 data centers while matching the power dispatch accuracy of the baseline.

Word-wise Fully Homomorphic Encryption (FHE) schemes, such as CKKS, are gaining significant traction due to their ability to provide post-quantum-resistant, privacypreserving approximate computing-an especially desirable feature in the Machine-Learning-as-a-Service (MLaaS) paradigm. In this work, we introduce FIDESlib, the first open-source server-side CKKS GPU library that is fully interoperable with well-established client-side OpenFHE operations. Unlike other existing open-source GPU libraries, FIDESlib provides the first implementation featuring heavily optimized GPU kernels for all CKKS primitives, including bootstrapping. Our library also integrates robust benchmarking and testing, ensuring it remains adaptable to further optimization. Comparing our scheme against Phantom (the previously top open-source CKK library, we show that FIDESlib offers superior performance and scalability. For bootstrapping, FIDESlib achieves no less than$70 \times$speedup over the AVX-optimized OpenFHE implementation. FIDESlib is available on Github11https://github.com/CAPS-UMU/FIDESlib.