The startup offers a data exchange platform that enables businesses to license data usage without sharing the actual data, ensuring data privacy and security. This approach allows companies to monetize their data while maintaining control over access and usage, mitigating legal and reputational risks.
Funding
$2.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Organizations struggle to unlock the full potential of their data due to privacy concerns, data silos, and the risk of exposing sensitive information. Sharing raw data for analysis can lead to compliance issues, reputational damage, and loss of control over data usage.
Solution
Shoji provides a data exchange platform that enables businesses to analyze and monetize data without directly sharing the underlying information. The platform employs techniques like federated learning, secure multi-party computation (SMPC), and differential privacy to allow for data analysis while ensuring data privacy and compliance with regulations like GDPR and CCPA. By keeping data within its original environment and utilizing encrypted computations, Shoji allows organizations to connect, compare, and analyze datasets from various sources without revealing row-level information. This approach facilitates breaking down data silos, creating new connections, and commercializing data assets in a secure and privacy-preserving manner.
Target Audience
Shoji targets organizations across various industries, including finance, healthcare, and government, that need to analyze sensitive data from multiple sources while adhering to strict privacy and compliance requirements.
Features
- Federated learning capabilities that enable sending models to the data instead of sending data to the models.
- Secure multi-party computation (SMPC) for jointly computing results from combined data without exposing the underlying data.
- Differential privacy to guarantee that outputs never reveal any personal information.
- Lightweight APIs for seamless integration with existing analysis tools like Python and SQL.
- Flexible data source integrations allowing virtual combination of data from multiple database and storage systems.
- Infrastructure-agnostic deployment options, including cloud, multi-cloud, and on-premises environments.
- Automated compliance with GDPR, CCPA, and other data protection regulations.