OVERVIEW

Achieve 99.9% Accuracy and 100% Lawful Repurposing

Organisations can now lawfully repurpose, share and combine personal data for analytics, AI & ML by leveraging Anonos BigPrivacy. BigPrivacy is a Pseudonymisation platform that retains 100% data utility and accuracy in insights, whilst ensuring compliance with the GDPR, CCPA, LGPD, and other emerging data protection laws around the globe.
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What is
Anonos BigPrivacy?
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Anonos BigPrivacy is a Pseudonymisation solution that transforms identifying source data into privacy-respectful data assets - the results are known as Variant Twins®. BigPrivacy uses state-of- the-art technical controls to embed privacy policies into the data, satisfying statutory and contractual requirements for lawful data use.
 
Feed your policies into the BigPrivacy engine to produce Variant Twins, State‑of‑the‑Art Sustainable Data Assets that:
Deliver the Resistance to Re-Identification of Anonymised Data
Retain the Lawful Controlled Re-Linkability of Pseudonymised Data
Preserve 100% of the Accuracy and the Full Utility of Source Data
Activate Express Statutory Benefits of GDPR Pseudonymisat-ion
Enable Lawful Secondary Processing
The Engine that Generates Patented Variant Twins
BigPrivacy gives a privacy engineer the tools to create Privacy Actions via a graphical user interface (GUI). Privacy Actions are specifically tailored to selected subsets of data fields/columns in a data source and integrate various privacy engineering techniques:
Lines
anonymisation techniques
  • Hashing, Tokenisation, Generalisation,
    Masking, Binning, Rounding, etc.
gdpr pseudonymisation
  • Separation of information value from identity
  • Linked by additional information
  • Store separately and control access to the additional information
controlled re-linkable dynamic de-identifiers
  • Apply dynamic de-identifiers, within and between datasets to defeat unauthorised relinking
  • Protecting not only direct, but also indirect identifiers to defeat inference attacks
re-identification risk
management (k-anonymity)
  • Manage risk of re-identification by suppressing records that do not meet the required threshold for k-anonymity
privacy actions
Variant Twins
BigPrivacy bundles these Privacy Actions to transform the source data record by record. This transformed data is filtered using k-anonymity, a re-identification risk management technique that prevents an adversary from defeating privacy protections via singling out.
Key
 
99.9%
Accuracy
Validated by Top 10 Global Bank
100%
Lawful Repurposing
For Analytics, AI, ML and Data Sharing
Lawful Machine Learning with No Compromises
Anonos partnered with a Top 10 Global Bank to demonstrate that pseudonymised Variant Twins deliver the same results in machine learning applications as clear-text source data.
DIGITAL TWIN TO VARIANT TWINS
Privacy-Respectful Data Assets Customised to Each Use Case
The flexibility of our approach enables a privacy engineer to create Variant Twins for different contexts, uses, and risks. Use case specific Variant Twins all originate from the source data, significantly enhancing downstream data fidelity, even on privacy-enhanced data.
The right-hand column shows a digital representation of the source data on John J. Jeffries. The Digital Twin includes direct identifiers like name and location, as well as indirect identifiers like date of birth, zip code, income and loan details. A privacy engineer designs Variant Twin A, “John”, to reveal the minimum data necessary to support an authorised use of the source data, whilst protecting the identity of the data subject.

All records included and revealed in a Variant Twin must satisfy a specific k-anonymity threshold to ensure there are a minimum number of records in each cohort. John's Variant Twin B has a higher likelihood of meeting a k-anonymity requirement and being included in the final output.
Privacy-Respectful Data Assets Customised to Each Use Case
 
Anonos’ Patented, Controlled Re-Linkable Dynamic De-Identifiers Significantly Advance Pseudonymisation
Traditionally, pseudonymisation policy has been one-dimensional. Deterministic pseudonymisation, where the same input value is always assigned to the same pseudonym, is applied to direct identifiers only. This static approach makes it useful as a localised security technique, but is vulnerable to linkage attacks, which prohibits combining datasets and practicing distributed analytics.
Anonos’ Patented, Controlled Re-Linkable Dynamic De-Identifiers Significantly Advance Pseudonymisation
In the image to the above, Anonos’ Patented Dynamism shows the same individual as different people across databases and tables. Dynamic de- identifiers, within and between datasets, defeat unauthorised re-identification - including linkage attacks and inference attacks. Because of this, organisations can feel confident the resistance to re-identification is robust and they can distribute data globally - ultimately repurposing personal data through self-service analytics and creating a data democracy.
the kicker
Achieve a Legal Basis to Repurpose Data whilst Ensuring 100% Accurate Insights
BigPrivacy embeds policies into the data to support GDPR-compliant Pseudonymisation and Data Protection by Design and Default to satisfy the balancing of interest test required for lawful Legitimate Interest processing. Most importantly, BigPrivacy can generate Variant Twins that deliver the same data value as clear-text source data, whilst providing GDPR-compliant privacy protection. The BigPrivacy engine unlocks new opportunities for business leaders, including:
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