Apheris partners with Gaia-X to shape the future of data ecosystems
Compliance, federation, data sovereignty, and trust are integral parts of everything we do at Apheris. We are proud to announce our partnership with Gaia-X.
The Apheris Privacy Guard creates data science workflows with built-in privacy protection
Apheris enables the secure analysis of data across organizations while keeping proprietary information private. Computations are executed locally – data never leaves the local environment and data privacy is preserved throughout the entire process. We use cutting-edge technologies to provide mathematical guarantees for privacy preservation.
Secure multiparty computation
Compute a joint function on multiple private inputs, where no party learns anything extra about other parties’ inputs.
Introduce random noise into results of queries on underlying confidential data, so that observers cannot reconstruct the original data.
Privacy preserving record linkage
Match data records that belong to the same entity (e.g. person) without revealing the identity of the entity.
Encrypt data such that computation is possible on the ciphertext – the decrypted result matches computation on plaintext.
Apheris services are GDPR ready and feature capabilities that enable our customers, and their data collaboration partners to comply with GDPR and other laws and regulations.
The Apheris Platform is architected and built for big data processing and supports cloud, multi-cloud and hybrid environments.
With industry-wide highest security standards, the Apheris Platform has been proven to protect enterprise grade-datasets at the world’s largest organizations.
Platform Use Cases
We see four main data collaboration setups which can unlock the full potential of the data
1. Collaborative data ecosystem – everyone contributes and consumes
2. Matching of data providers and consumers – AI model provider consumes third party data
3. Pre-collaboration assessment – data and model providers test complementary assets while preserving IP
4. Trained models off the shelf – securely commercialize data & data products