Leverage the potential of sensitive data without compromising privacy with secure data science platform integrated into your existing workflow.
Take Full Control of Your Data
Empower data scientists and machine learning models to work on sensitive data without compromising individual privacy.
AGENT broadens enterprise data access by unlocking unutilised sensitive data stuck in silos due to privacy restrictions. It ensures comprehensive data availability for informed decision-making, helping optimise processes and boost revenue.
By making individual information inaccessible to even the people working with data, AGENT ensures that your data remains sanitised and doesn’t land anywhere it’s not meant to go.
Balance the individual data protection with valuable aggregate analysis by customising epsilon and delta to your needs and foster seamless collaboration between teams, departments, and organisations.
Instead of relying solely on traditional trust models, AGENT integrates differential privacy measures to provide quantifiable guarantees of data protection while still allowing for the extraction of valuable insights.
AGENT serves as a bridge between data scientists and data sources, applying strict privacy standards through differential privacy to guarantee that all queries and analytics don't compromise individual privacy.
Specialised Restricted Version of Python
Leveraging custom Jupyter Kernels, you can perform secure code execution in your notebook within an isolated environment, our specialised version of Python, known as Private Python.
True Data Anonymisation
Differential Privacy works by injecting a certain amount of 'noise' into the dataset to obscure individual data points and enable true anonymisation, as utilised by US Census and United Nations.
Full Enterprise Control and Transparency
Easily track your team's progress, manage access and privacy settings, and budget efficiently with our user-friendly AGENT platform designed for seamless organisational collaboration.
The Best-in-class Disclosure Control Frameworks
AGENT enforces the best-in-class disclosure control frameworks influenced by innovations from institutions like Harvard, Microsoft, and IBM Research which ensure only Differentially Private results can be accessed, printed, or exported.
Do you want to know more specific information or see how Antigranular Enterprise works? Contact our team to show you what it can do for your business.
What is the difference between OBLV Deploy and Antigranular Enterprise?
OBLV Deploy is made for businesses looking to deploy applications in a secured computing environment. AGENT is for organisations who want to enable data scientists and machine learning models to work and collaborate on sensitive data without compromising individual privacy.
What guarantees do you offer ensuring that my sensitive data won't be compromised?
Antigranular Enterprise combines multiple security controls and privacy-enhancing technologies to ensure data privacy. Antigranular Enterprise is built with end-to-end encryption and confidential computing as an input privacy technique to ensure no direct access to data. This is combined with differential privacy as an output privacy mechanism allowing only for aggregate queries with a controlled amount of 'noise' added to the output of a computation to prevent reverse-engineering of input data.
Is Antigranular Enterprise customisable to suit individual business needs?
Yes, our solutions are designed to cater to a wide range of users. Antigranular Enterprise is capable of handling both highly sensitive data and high volumes, providing a robust and scalable solution for businesses of varying sizes and needs.
Can I try Antigranular?
Yes, you can join the Antigranular platform to explore its functionality using our sample datasets.
What is differential privacy?
Differential Privacy (DP) is a framework designed to ensure individual data remains private when conducting statistical analyses. It achieves this by introducing controlled random noise into data queries, obscuring the impact of any...Read more
Why is de-identification insufficient for ensuring privacy?
De-identification, the process of removing or masking identifiable information from a dataset, is insufficient for ensuring privacy because it can be vulnerable to re-identification. This vulnerability arises when de-identified data is combined...Read more
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