Privacy-Preserving Federated Analytics: Secure Aggregation Protocols, Homomorphic Encryption Integration, and Scalability Analysis for Cross-Organizational Data Collaboration
Privacy-preserving analytics across organizational data siloes -- enabling statistical insights from combined datasets without any party sharing raw data -- requires cryptographic protocols that provide formal privacy guarantees while remaining computationally feasible at the scale of real analytical workloads. This paper presents a systematic engineering evaluation of three privacy-preserving analytics approaches -- Secure Aggregation (SecAgg), Partial Homomorphic Encryption (PHE) using the Paillier cryptosystem, and Fully Homomorphic Encryption (FHE) using CKKS scheme in the Microsoft SEAL library -- for four representative analytical query types: count and sum aggregation, histogram construction, linear regression, and gradient-boosted tree inference. Experiments are conducted with up to 100 participating organizations on a WAN-simulated testbed, measuring query latency, communication overhead, and accuracy loss from approximation or noise addition. SecAgg achieves the best latency for aggregation queries (mean 1.4 seconds for 50-party sum) with no accuracy loss, but does not support non-linear computations. PHE supports linear regression at 50-party scale in 8.2 seconds with zero approximation error. FHE-CKKS enables approximate gradient tree inference at 50-party scale in 94 seconds, with 0.8 percent mean accuracy loss from CKKS approximation. We introduce the Privacy-Analytics Performance Index (PAPI) that aggregates latency, communication cost, accuracy retention, and implementation complexity into a single score, and provide a cryptographic protocol selection guide for 12 common multi-party analytics scenarios.
