Abstract: A method and system for encryption and assured deletion of information is provided, the method at least includes: sorting fields of the information into at least two sensitivity levels by sensitivity; generating encryption keys and key shards thereof based on predetermined thresholds, and creating mapping between targets and the key shards, based on the encryption keys for the sensitivity levels, encrypting the information fields of the corresponding sensitivity levels and deleting the original information and encryption keys; and in response to reception of a recover request, recovering the encryption keys based on the key shards and performing decryption, so as to recover the original information. The present disclosure aims at the problem that information is difficult to be safely stored and assuredly deleted, and realizes multi-party security key deletion of encrypted personal information.

Abstract: A method and system for trusted third-party audit of personal-information deletion is provided, wherein the method includes: acquiring log data of an arbitrary source-domain personal-information deleting body in a network and of its associated-domain personal-information deleting bodies; normalizing the log data according to predetermined parsing rules and thereby generating normalized log data; and performing consistency-of-notification analysis and operation-compliance analysis on the normalized log data by means of association analysis. The present application is based on an audit analysis of log files to identify whether the information deletion process conforms to the multi-dimensional or multi-level audit judgment conditions and to perform forensics on abnormal deletion of personal information in a timely manner, so as to ensure that personal-information deletion can satisfy requirements of internal control, industrial standards, policies and regulations.

Abstract: This disclosure relates to improved sketch-based image retrieval (SBIR) techniques. The SBIR techniques utilize a neural network architecture to train a domain migration function and a hashing function. The domain migration function is configured to transform sketches into synthetic images, and the hashing function is configured to generate hash codes from synthetic images and authentic images in a manner that preserves semantic consistency across the sketch and image domains. The hash codes generated from the synthetic images can be used for accurately identifying and retrieving authentic images corresponding to sketch queries, or vice versa.

Abstract: This disclosure relates to improved sketch-based image retrieval (SBIR) techniques. The SBIR techniques utilize a neural network architecture to train a domain migration function and a hashing function. The domain migration function is configured to transform sketches into synthetic images, and the hashing function is configured to generate hash codes from synthetic images and authentic images in a manner that preserves semantic consistency across the sketch and image domains. The hash codes generated from the synthetic images can be used for accurately identifying and retrieving authentic images corresponding to sketch queries, or vice versa.