CyberVein’s Encrypted Computation Ensures Data Privacy and Helps Facilitate Data Technology Development
As Information technology matures, data plays an increasingly significant role for both individuals and authorities. Statistics show that the big data market makes $7 billion more profits every year, an annual increase of 14%. However, at the same time, data leakage is also raising concerns. Around 2500 private data leakages were reported last year, causing 14.8 billion important documents exposed and 5+ million loss for each case. This has made data flow and sharing more difficult than ever, in that such leakages not only cause economic loss, but also dampen the public’s faith in data sharing. Against this backdrop, CyberVein carries the mission of encouraging the flow of data while ensuring its security.
An Update on Data Protection Solutions
Estimates put the value of global big data market at $103 billion in 2027, twice the size of 2018. Data from applications will worth $46.4 billion, accounting for 45% of the total value. CyberVein views data as both valuable assets and motivation for smart city development, therefore rolling out DAVE to grab market opportunities and make data more valuable.
Built to analyze data and undiscover its value, DAVE consists of PISR Database, DAG Storage Chain, Cytrix and Federated Learning. Encrypted computation is used in both Cytrix and Federated Learning to interconnects data from different fields and enable free flow of information between cities.
Encrypted Computation Helps Protect Data Privacy
During encrypted computation, data is pooled from different sources in a most secure way. This technology protects data ownership and usage by separating them through Cytrix and Federated Learning, and at the same time. Along with other technologies like homomorphic encryption, garbled circuit and MPC, it makes data compliant, usable and valuable.
1. Homomorphic encryption enables computation and analysis of encrypted data.
2. Garbled Circuit secures input and output data by encrypting all the gates of the circuit and putting them in a random order. Decryptors can only see computing results.
3. MPC makes sure that results computed based on ciphertext consistent with that on plaintext, and that parties can decide the function of data without needing to decrypt them.
4. Federated Learning encourages mutual learning within the alliance and supports co-modelling without revealing bottom-layer data.
CyberVein Dedicates Itself to Data Protection and Seeks All-win Outcomes
As data becomes more valuable and data security becomes increasingly salient, encrypted computation will be a firm support for user data protection and data application. CyberVein will tap into more fields and make concrete achievement, injecting new energy into the development of both smart city and digital economy.