Private Data Exchange
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.
Frequently asked questions
Find quick answers to common questions using our helpful FAQs.
Security needed will be minimal.
It is recommended that a company just protects the original data with enough security such that only the privacy program can access it to produce privatized data. If necessary, a company can also install security measures to prevent theft of the privatized data, which exposes data trends. Even if the privatized data falls into the wrong hands, it is very unlikely that individual privacy will be exposed.
Our software is compatible with most security systems.
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.
Companies using our product
The Pridatex Anonymizer
The Perfect Anonymization Tool for Any Data Size
The Pridatex Anonymizer randomizes and obfuscates data, instead of generating new data, so that the resulting data cannot be linked back to the real individuals whose data is anonymized. Our anonymizer relies on the mathematical theory of Differential Privacy to prove that the anonymous data cannot be re-identified and that it is strongly compliant with data protection laws. The anonymous data that the Pridatex Anonymizer produces is highly realistic and accurate to the original sensitive data, no matter what the size of the original data is. This means that all businesses, no matter their size, will not have to share or work with sensitive data anymore.
How Pridatex is Disrupting the Data Anonymization space
Differential Privacy is 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 does so by obfuscating the 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 much 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:
- Such data often underperform substantially in machine learning compared to real-world data.
- Its reliance on generative methods in Machine Learning makes it impractical for modeling small data sets.
- 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.
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.