Hangzhou City University

Zhejiang Gongshang University

Zhejiang University

Beihang University

Hangzhou Hyperchain Technology Co., Ltd.

Zhejiang Ponshine Information Technology Co., Ltd.

Siqi Ren

Song Han

Minghui Wu

Shuai Zhao

Xuanxuan Xu

Jinsong Han

Zhibo Wang

Yuxuan Fang

Xiaoli Chen

Xiling Luo

Liang Cai

Shen Wang

Jianhong Lin

Luyao Wang

Shudan Jin

The present disclosure provides a data maximum/minimum value (max/min) aggregation method and system for privacy protection. A Paillier cryptosystem is used to encrypt user data, so as to protect privacy and security of user health data. Further, time aggregation is implemented to help a control center obtain an overall health condition of a client in one aggregation period. By using a homomorphic property of the Paillier cryptosystem, the present disclosure implements aggregation of a max/min of privacy protection, and helps the control center obtain a max or a min of health data of the client in the aggregation period.

Data maximum/minimum value aggregation method and system for privacy protection

A dynamic and verifiable searchable encryption method based on updatable encryption and blockchain and a system thereof are provided. The method is suitable for the real scene where the data user and the data owner are not the same party. The method adopts the updatable technology to update the secret key in the searchable encryption, and the security of the key update could achieve Chosen Ciphertext Security (CCA) and Ciphertext Integrity (CTXT) security. In addition, the method is used to judge whether the cloud server is 'trustworthy' by verifying the search results publicly based on the blockchain. Therefore, the verifiable dynamic searchable encryption method based on updatable encryption and blockchain has been greatly improved in terms of practicability and security.

Dynamic and verifiable searchable encryption method and system based on updatable encryption and blockchain

A privacy protection method and system for financial data sharing based on federated learning are provided. In recent years, due to the restrictions of data security and privacy protection laws and regulations, it is difficult to share data across institutions or departments. In order to make data transfer and transaction between different entities can be achieved without violating the national laws on data privacy and data security, the privacy protection method and system for financial data sharing based on federated learning is provided. A privacy collection intersection technology is adopted, so that two institutions, which may have many differences in business, but most of their customer groups are the same, jointly train a learning model.

Privacy protection method and system for financial data sharing based on federated learning

Growing community of inventors

Hangzhou, China

Siqi Ren