Rep4, SPDZ-wise, MNIST training

• Rep4: honest-majority four-party computation with malicious security
• SY/SPDZ-wise: honest-majority computation with malicious security based on replicated or Shamir secret sharing
• Training with a sequence of dense layers
• Training and inference for multi-class classification
• Local share conversion for semi-honest protocols based on additive secret sharing modulo a power of two
• edaBit generation based on local share conversion
• Optimize exponentiation with local share conversion
• Optimize Shamir pseudo-random secret sharing using a hyper-invertible matrix
• Mathematical functions (exponentiation, logarithm, square root, and trigonometric functions) with binary circuits
• Direct construction of fixed-point values from any type, breaking sfix(x) where x is the integer representation of a fixed-point number. Use sfix._new(x) instead.
• Optimized dot product for sfix
• Matrix multiplication via operator overloading uses VM-optimized multiplication.
• Fake preprocessing for daBits and edaBits
• Fixed security bug: insufficient randomness in SemiBin random bit generation.
• Fixed security bug: insufficient randomization of FKOS15 inputs.
• Fixed security bug in binary computation with SPDZ(2k).