Sharemind MPC is a secure data processing platform that allows you to collaboratively analyze information without revealing the underlying details. Imagine multiple parties working together on a project, but each keeps their own data private. Sharemind MPC makes this possible using a technique called secure multi-party computation (MPC).
A practical example where financial institutions perform collaborative analyses without sharing their data.
- Before uploading the confidential data to Sharemind, data owners protect their data by secret sharing it. This provides similar guarantees as encryption.
- Sharemind MPC server hosts cannot see any individual uploaded values.
- Sharemind MPC can process the data without ever reconstructing the user inputs. All intermediary results are protected as well.
In addition, Sharemind MPC servers collaboratively ensure that no unauthorized query can be performed and only authorized users can provide data or see results. Logs are kept of all activities to keep track of queries and users.
This approach ensures end-to-end data protection and privacy, making Sharemind MPC a valuable tool for situations where collaboration requires confidentiality.
Built-in data analytics capabilities
Rmind is the secret weapon of Sharemind MPC, the data analysis suite that unlocks insights from protected data. Rmind allows analysts to perform a wide range of statistical tasks on encrypted datasets hosted on Sharemind.
Heatmap - a privacy-preserving version of a scatterplot
payment_history <- load("DS1",
"payment_history")
heatmap(payment_history$amount,
payment_history$date_diff)
Rmind's familiarity with the R programming language makes it user-friendly for statisticians already comfortable with R. Analysts can leverage Rmind's capabilities to conduct various analyses, including data manipulation, statistical modeling, and even data visualization – all while the individual data points remain securely encrypted. This ensures privacy for data owners while empowering researchers to extract valuable insights from combined datasets.
- Flexible API
The Application Server provides an Application Programming Interface (API) for implementing privacy-preserving services. These apps can be used directly from mobile or web applications by end users. Alternatively, they can be used from application backend.
- Less complexity
The Sharemind Client API takes the cryptographic complexity out of building privacy-preserving user interfaces. It automatically handles encryption, upload and queries for the client application. At the same time, the open source SecreC Standard Library of privacy-preserving algorithms reduces the time of developing the service backend.
Something for everybody
| Core | Analytics | Cloud Platform | |
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| Value proposition What can it handle? |
Integrate an efficient MPC implementation of a function into your service. | Quickly develop and integrate complex analytical MPC functionality into your service | Deploy a scalable MPC-as-a-Service platform with on-demand availability |
| Target users Who is the runtime designed for? |
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| Benefits to MPC implementers What makes it special? |
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| Components What makes it tick? |
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Aspects of Core and Analytics |
