Privacy-preserving data evaluation is one of the prominent research topics in the big data era. In many data evaluation applications that involve sensitive information, such as the medical records of patients in a medical system, protecting data privacy during the data evaluation process has become an essential requirement. Aiming at solving this problem, numerous fuzzy encryption systems for different similarity metrics have been proposed in literature. Unfortunately, the existing fuzzy encryption systems either fail to achieve attribute-hiding or achieve it, but are impractical. In this paper, we propose a new fuzzy encryption scheme for privacy-preserving data evaluation based on overlap distance, which can work in an integer domain while achieving attribute-hiding. In particular, we develop a novel approach to enable an accurate overlap distance to be fast calculated. This technique makes the number of pairing operations during decryption stage negative correlation with the size of
On July 25, the Departments of Labor, Health and Human Services, and the Treasury (Departments) jointly issued highly anticipated guidance on mental health and substance use disorder.
Seyfarth Synopsis: The Mental Health Parity and Addiction Equity Act (MHPAEA) requires group health plans and insurers to cover treatments for mental health and substance use disorders.