Inventec Corp.

Inventec (pudong) Technology Corporation

Feng-Hao Liu

Wei-Chao Chen

Chih-Fan Hsu

Ming-Ching Chang

Yu Te Ku

Jing-Lun Huang

Shih-Hao Hung

Ke-Han Li

The present disclosure provides a system including first to second electronic devices. The first electronic device includes a first processor configured to encrypt a machine learning model to generate an encrypted machine learning model. The second electronic device includes a second processor configured to receive the encrypted machine learning model; and a trusted processing circuit configured to decrypt the encrypted machine learning model to get the machine learning model. The third electronic device includes a database configured to store public keys; and a third processor configured to perform a check operation to check whether a first public key of the trusted processing circuit is in the public keys to determine whether the trusted processing circuit is valid. The first processor is further configured to transmit the encrypted machine learning model to the second processor according to the trusted processing circuit determined valid.

System and method for executing machine learning model inference

A system for verifying edited image includes: a producer terminal device configured to tile a source image for a plurality of smaller tiled images with individual source image hash values to accordingly calculate an integrated source image hash value, and to execute digitally signing to generate an image tag pair; an editor terminal device configured to receive the image tag pair, to divide the source image into these smaller tiled images according to a tile configuration, to edit part of the smaller tiled images, to include the rest part of these smaller tiled images to generate an edited integral image and further calculate an integrated edit image hash value, and to execute digitally signing to generate a zero-knowledge proof (ZKP) assurance; and, a user terminal device configured to receive the ZKP assurance to accordingly verify whether or not the edited integral image is generated by editing the source image.

System for verifying edited image

The federated learning system includes a moderator and client devices. Each client device performs a method for verifying model update as follows: receiving a hash function and a general model; training a client model according to the general model and raw data; calculating a difference as an update parameter between the general model and the client model, sending the update parameter to the moderator; inputting the update parameter to the hash function to generate a hash value; sending the hash value to other client devices, and receiving other hash values; summing all the hash values to generate a trust value; receiving an aggregation parameter calculated according to the update parameters; inputting the aggregation parameter to the hash function to generate a to-be-verified value; and updating the client model according to the aggregation parameter when the to-be-verified value equals the trust value.

Method for verifying model update

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.

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Taipei, Taiwan

Feng-Hao Liu