Framework for privacy-preserving data sharing
For organizations that need access to third-party data while protecting their IP, Apheris creates a privacy-preserving data ecosystem
Decentralized analysis on distributed datasets. Data stays under the full control of the data owner
Our privacy engine is built upon breakthrough innovations in the fields of cryptography, data privacy and federated machine learning
How our product works
Moving algorithms instead of data
We empower data scientists to access and compute on third-party private data, without compromising or revealing the underlying data.
Our core privacy engine is empowered by cutting-edge privacy technologies
Apheris enables companies to train AI models on distributed data while fully preserving data privacy. 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.
Differential privacy
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.
Homomorphic encryption
Encrypt data such that computation is possible on the ciphertext – decrypted result matches computation on plaintext.
Platform architecture
Enterprise grade security: scalable computing platform built upon the latest security standards
Ease of use
Seamless deployment
Scalable
Key features
Protect data privacy and intellectual property of data
Industry-leading privacy-preserving technology for the most complex algorithms
Industry-leading federation technology
Enterprise-grade security
Tested and trusted
