Privacy Computing HSM

PRODUCT OVERVIEW

Privacy Computing HSM is a data privacy protection solution that integrates computing power layer, hardware security layer, and pre-set scenario software, aiming to provide various privacy computing services. Privacy Computing HSM combines high-performance cryptographic chips with secure computing algorithms to provide data security protection and reliable performance. On the basis of satisfying data privacy and security, data can be calculated and analyzed to achieve the flow and sharing of data value, achieving the effect of “Original data not leaving the domain. Data available but invisible”.

PRODUCT FEATURES

Privacy Set Intersection

The intersection of privacy sets allows two parties holding their own sets to jointly calculate the intersection of two sets. At the end of protocol interaction, one or both parties should obtain the correct intersection and not receive any information from the other party’s set outside of the intersection.

Private Information Retrieval

Private Information Retrieval can retrieve specific information from a database without exposing other irrelevant information, and can be used to protect user privacy and prevent database holders from inferring sensitive user information.

Federated Learning

Federated learning can combine multiple data sources to enhance the effectiveness of machine learning models while ensuring data privacy and security, protecting terminal and personal data privacy, and ensuring legal compliance.

Data Preprocessing

Data preprocessing is to clean, process, and transform raw data for better model training and data analysis. Mainly includes: data cleaning, feature selection, feature scaling, feature transformation, data partitioning, label encoding, feature engineering, and data visualization.

Customized functions

Other functions that can be customized and configured based on user needs and business changes

PRODUCT ADVANTAGES

Data Security Protection

Adopting multiple encryption algorithms and security protocols to ensure the security of data during transmission, storage, calculation and analysis. Participants are unable to infer the input data of other parties through the interaction data during the calculation process, thus protecting the privacy and integrity of data.

Flexibility and Scalability

Privacy computing products have high flexibility and scalability, and can be customized and configured according to user needs and business changes. Users can choose appropriate algorithms and functional modules based on their actual needs to achieve customized data collaboration scenarios.

Data Available but Invisible

Promote data circulation and sharing, achieve cross organizational, cross industry, and cross regional cooperation of data, break the phenomenon of data silos, and improve the value and efficiency of data utilization.

Collaborative Data Computing

Implement collaborative computing among multiple participants to improve computational efficiency and accuracy. Participants can share data through privacy computing products and work together to complete computing tasks while maintaining data privacy.

APPLICATION CASES

Healthcare

Privacy Computing HSM can be used for medical data analysis. Medical institutions can store patients’ sensitive data in Privacy Computing HSM and then process the data, such as diagnosis, disease risk prediction, etc., without the need to transmit the data to external servers. This approach can protect the privacy of patients while providing valuable data for medical research and decision-making.

Finance

Privacy Computing HSM can assist financial institutions in processing users’ financial data, such as conducting risk assessments, credit scores, etc. Users’ sensitive data can be securely stored in HSM, while allowing necessary computing operations to be performed without the need to transmit data to third-party service providers.

Smart City

Privacy Computing HSM can be used to process large-scale sensor data, such as traffic flow, environmental monitoring, etc. These data can be processed and analyzed locally to provide real-time city operation status and improvement suggestions, while protecting residents’ personal privacy.

AI Research

Researchers can use Privacy Computing HSM to process sensitive data for training and improving machine learning models without the need to transfer data to external cloud servers. This can promote the development of machine learning and artificial intelligence while protecting data privacy.