Accelerating Privacy-Preserving Machine Learning With GeniBatch
X. T. Huang, Junxue Zhang, Xiaodian Cheng, Hong Zhang, Yilun Jin, Shuihai Hu, Han Tian, Kai Chen · 2024
Cross-silo privacy-preserving machine learning (PPML) adopt; Partial Homomorphic Encryption (PHE) for secure data combination and high-quality model training across multiple organizations (e.g., medical and financial). However, PHE introduces significant computation and communication overheads due to data inflation. Batch optimization is an encouraging direction to mitigate the problem by compressing multiple data into a single ciphertext. While promising, it is impractical for a large number of cross-silo PPML applications due to the limited vector operations support and severe data corruption.