Abstract: The present disclosure provides a privacy computing method based on homomorphic encryption, which includes steps as follows. The ciphertext data is received, where the ciphertext data has a floating-point homomorphic encryption data structure, and the floating-point homomorphic encryption data structure of the ciphertext data includes the ciphertext mantissa, exponent parameter and gain parameter. The gain parameter sets the precision of the floating point corresponding to the ciphertext mantissa. The exponent parameter is adapted to multiplication or division. The artificial intelligence model performs operations on the ciphertext data to return the ciphertext result.

Abstract: An exemplary socket assembly includes a circuit plate and a socket mounted on the circuit plate for fixing an IC. The socket includes a first fixing member and a second fixing member. The first fixing member includes installed portions, elastic connecting portions, and a first buckling portion, the elastic connecting portions interconnects the electric portions and the first buckling portion. The second fixing member includes electric portions, elastic connecting portions, and a second buckling portion, and the elastic connecting portions interconnect the electric portions and the second buckling portion.

Abstract: An exemplary socket assembly includes a circuit plate and a socket mounted on the circuit plate for fixing an IC. The socket includes a first fixing member and a second fixing member. The first fixing member includes installed portions, elastic connecting portions, and a first buckling portion, the elastic connecting portions interconnects the electric portions and the first buckling portion. The second fixing member includes electric portions, elastic connecting portions, and a second buckling portion, and the elastic connecting portions interconnect the electric portions and the second buckling portion.