A foundational underpinning of Anonos' first-of-its-kind BigPrivacy technology is that “Big Data” requires a new scalable, privacy-preserving approach to enable organizations to process data while managing security and privacy concerns.

Anonos BigPrivacy enforces data-centric security to de-risk data to enable it to be lawfully used, shared, compared and computed to maximize value.

BigPrivacy technology also enables two (or more) parties having private data inputs to perform analytics on the combined data so that each party learns the correct output and nothing else. The objective is to run algorithms on the union of the parties' private data without allowing any party to view the other party’s private data.

For data-driven companies, this is imperative to maximize the value of data assets.

BigPrivacy technology enables organizations to benefit from data insights derived from analytics while keeping personal data secure and private.

The figure above shows how BigPrivacy does not require changes to existing systems to accomplish these objectives. For each party willing to share data, BigPrivacy takes source data in their existing format and transforms the data into a harmonized de-identified/pseudonymized format. BigPrivacy converts source data into de-Identified data. As the data is de-identified, BigPrivacy simultaneously converts it into a harmonized format to enable use, sharing, comparing and computing among disparate parties. Anonos BigPrivacy supports functional interoperability to enable parties to use, share, compare and compute data without requiring syntactic interoperability, semantic interoperability or relying on non-technically enforced data sharing arrangements.
Anonos BigPrivacy introduces improvements over other privacy preserving techniques. 

For Example:

  • Differential Privacy – Differential Privacy (“DP”) is focused on trade-offs between data utility and information leakage. With DP, the more that individual privacy is protected, the less accurately you can compute aggregate statistics about the collection. When using DP, high-level global statistics are possible, but detailed accurate data is not. BigPrivacy decreases the need to distort, delete, or otherwise vitiate the data when processing, as happens with differential privacy.
  • Homomorphic Encryption – Homomorphic Encryption (“HE”) is considered by many to be the “holy grail” for researchers because of its potential to enable comparison of separate, fully encrypted, sensitive datasets without revealing underlying sensitive data. However, HE does not support sophisticated analysis like data mining because it does not support mathematical operations necessary to perform this detail level of processing. Conversely, BigPrivacy supports data mining at a detailed level and re-linkability to identifying data under controlled conditions.
Anonos BigPrivacy increases the accuracy of analytics, artificial intelligence (AI) and Machine Learning (ML) while still supporting the ability to relink back to identifying information under technically controlled authorized conditions.




Anonos IP/Patents*

Incorporate data decoupling/fine-grained, purpose-specific data use controls into your architecture

BigPrivacy® Virtual Machine*

Works with most ETL systems(on premise / cloud / hybrid)

BigPrivacy® Engine on Premise/Cloud/Hybrid)*

  • API
  • Plug-in (to add “Anonosizing” functionality to applications)
  • Proxy (leveraging existing protocols to “Anonosize” data)

* All deployment models enable descriptions of patented BigPrivacy dynamic anonymization, pseudonymization and de-identification technical and organizational measures to be included in GDPR Article 13 and 14 required information made publicly available regarding the purpose and legal basis for processing personal data and steps taken to pursue legitimate interest as a legal basis for use by a controller or third party for GDPR and all other evolving regulations.