Achieve Regulatory Compliance
with State-of-the-Art Anonymization
at a Fraction of the Cost

Private Data Exchange

Complying with data protection regulations like CCPA, GDPR, & HIPAA is expensive.
Reduce your compliance costs by 75% using our Differential Privacy-based software.

Pridatex improves upon Differential Privacy to allow you to share granular customer data in a way that provably protects individual privacy. Our software helps companies permanently comply with data protection laws, broadens data innovation, and democratizes data science.

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Frequently asked questions

Find quick answers to common questions using our helpful FAQs.

Databases that can format information in terms of rows and columns. So, most databases that store metadata.

Sure, we can integrate it into any setting you have in mind.

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The Pridatex Anonymization Engine

Introduction

The Pridatex Anonymization Engine is a software tool that automatically produces provably anonymous (“scrambled”) copies of personal user data. The software ensures that the data in an anonymous copy cannot be linked and traced back to any individual in the personal user data. The software also ensures that the machine learning value of the personal user data is preserved in an anonymous copy.

Business Value

Since data protection laws don’t apply to anonymous data, our software reduces compliance costs by 75% for any business. This translates to a savings of $4.13 million annually for the average large business.

Legal Framework

Differential Privacy has been recommended by the IAPP for meeting the high bar of anonymization—it helps companies remain compliant even with the strictest of privacy audits. By anonymizing sensitive data, the Pridatex Anonymization Engine helps companies bypass data protection regulations like GDPR as per Recital 26 of GDPR. Similar regulations such as CCPAHIPAA, and international data protection laws like LGPD (Brazil) are gradually adopting GDPR standards in exempting anonymous data from data protection regulations. Additionally, anonymous data is exempt from transfer-of-data regulations such as Schrems II

Technical Summary

In order to produce anonymous copies of personal user data, our software relies on the mathematical framework of Differential Privacy. Unlike some contemporary methods, this anonymous data not only retains the structure and individual format of the personal user data but also preserves 95–99% of the personal user data’s machine learning accuracy. Our software can be integrated as an API, a plugin, or a standalone application.

Solutions: How our software can be used

To allow storing data indefinitely without deletion

  • Keep valuable user data for analysis even after retention period of privacy policy has expired.
  • Compliantly retain valuable, yet non-personal, user data even after users request deletion of their data.
  • Store anonymized datasets pooled from multiple countries to be used indefinitely for purposes of data analysis.

To facilitate sharing data with third parties & transferring it overseas

  • Share data with third parties worldwide, while complying with laws like Schrems II, for optimal outsourcing.
  • Share real-world data with third parties to speed up POCs.
  • Share locally-produced data with international teams or servers across international borders.

To facilitate processing valuable data without analytical restrictions

  • Process valuable anonymous data while complying with the Right to Object to Processing under GDPR by not using personal user data.
  • Process valuable anonymous data without any restrictions to data analysis.
  • Automate decision making with valuable anonymous data while complying with user requests for not using their personal data.

Impacts

Pridatex allows all companies to share their data across corporate divisions and international borders without worrying about privacy violations and data protection regulations. It makes the threat of data breaches a thing of the past and clears the path to true data democratization.

Pridatex also allows all individuals in a society to preserve their fundamental right to privacy and yet help both themselves and their community benefit from the utility of their data.

Our benefits to both companies and society include, but are not limited to

Compliance with Data Protection Laws

Strongly comply with even the strictest regulations like GDPR and HIPAA using the proven privacy-preserving capabilities of Differential Privacy.

  • The foundations of Differential Privacy are well accepted by international data protection law.
  • Eliminate the threat of data breaches - even if a data breach occurs, your company will still be protected from the legal and financial repercussions.
  • Retain the same data utility as the original data.

Broadening Data Innovation

Unlock your data assets within your organization to capitalize on communication between different teams.

  • Eliminate the need for encrypting and decrypting sensitive data that could be accessed by only select individuals.
  • Eliminate the need for costly encryption key management.
  • Broaden access to sensitive data for teams and employees to freely cross-collaborate and communicate with one another.

Democratizing Data Science

Open the market up for data science to access innovative data analytics solutions quickly.

  • Broaden access to data for competing and cooperating third parties and freelancers to analyze.
  • Be able to share your data with data analysts worldwide to access great solutions fast.
  • Overcome the data localization laws of a country that restrict you from sharing data outside the international borders of that country.

Technical Details

The Pridatex Anonymization API is based on a novel and academically-verified use of Differential Privacy, a mathematically proven approach to data anonymization that allows machine learning methods to draw conclusions and insights from user datasets while protecting the privacy of each individual in that dataset. It applies this method by obfuscating individual data—mixing it with artificial privacy-preserving noise in a way that prevents the unmasking of individuals in the dataset and thwarts malicious attempts to trace any data point back to an identifiable source. 

Contemporary methods in Differential Privacy mix the results of a query on the dataset with artificial privacy-preserving noise (Method A) or mix the user data with artificial privacy-preserving noise before returning the results from the query (Method B), as shown above. In either case, a query on the dataset produces a result that is an obfuscated aggregated statistic of the data. This creates a data utility problem because aggregated statistics of the data cannot provide as many granular insights on individuals as the original data can. Furthermore, data scientists cannot train their models as well with aggregated statistics of the data as they can with individual data. 

A popular alternative to Differential Privacy is Synthetic Data Generation because it does not sacrifice as much utility as contemporary methods in Differential Privacy. Synthetic Data Generation utilizes machine learning to create a model from the original sensitive data and then generates new fake aka “synthetic” data by resampling from that model. However, Product Managers in top-tech companies like Google and Netflix are hesitant to use Synthetic Data because:

  1. Such data often underperform substantially in machine learning compared to real-world data.
  2. Its reliance on generative methods in Machine Learning makes it impractical for modeling small data sets. 
  3. It may not adequately preserve privacy according to numerous studies. 

Companies, therefore, find themselves in a dilemma—either use Synthetic Data to analyze big data, thereby sacrificing some data privacy and utility, or violate data privacy laws, thereby assuming all the associated legal and financial risks arising from such action. Companies need an anonymization solution that can preserve data utility on any size of data and that can guarantee privacy.

Pridatex solves this problem by applying the fundamental concepts of Differential Privacy to obfuscate data on individuals and produce granular individual data without compromising privacy or accuracy. Unlike contemporary methods in Differential Privacy, our anonymization method does not rely on queries and therefore the problem of aggregated statistical results is circumvented. Unlike the fake data generation process of Synthetic Data, the mixing of data with artificial privacy-preserving noise in our method retains characteristics of the original data. Thus Pridatex preserves full data utility for each individual data while guaranteeing privacy. Some top tech companies are considering us as a replacement for their current form of anonymization because of our advantages over both classical Differential Privacy and Synthetic Data, shown below.

Feel Free to Contact Us

If you have any questions about our product, want to schedule a demo, or require additional support, please get in touch with us using the form below.