Event Horizon: Next Generation Anonymization for Regulatory Compliant Data Sharing

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Event Horizon ‚ÄĒ Context based anonymization for businesses to unlock data with mathematical privacy guarantee

How do you protect ‚Äėdata in use‚Äô after sharing it?

One of the biggest challenge faced by CISOs & DPOs in today‚Äôs world is how to protect ‚Äėdata in use‚Äô, especially when an organization shares data beyond its perimeter? A hint: Answer is not encryption or access control.

While encryption and authentication technologies can protect data at rest and in transit, these technologies are not built for protecting data after sharing or ‚Äėdata in use‚Äô, because once someone decrypts the data for the purpose of processing, he can use it for purposes beyond what is agreed upon and data provider does not have any control on it. Hence the solution needs to be protecting data in a much more fundamental way rather than just adding a layer like encryption to it.

Enter Privacy Enhancing Technologies (PET). These technologies can protect data in use, which was not possible earlier. They fall into two categories ‚ÄĒ Output privacy, which are more statistical in nature and input privacy technologies, which are more cryptographic in nature. PET can protect data in use, which is considered holy grail of data processing of the future.

Event Horizon‚Äôs ‚ÄĒ Differentially Private Generealization

Traditional technologies like pseudonymization, tokenization, masking or encryption are not considered to privacy preserve a dataset, as data subjects can still be singled out or the whole process can be reverse in the future, resulting in compromise of privacy of data subjects. Hence data processed with these techniques are still considered personal data.

To solve this problem, there are a few methods of privacy preservation like k-anonymity or differential privacy, but achieveing a balance between utility and privacy protection has been a challenge. At PrivaSapien, we have invented and patented a breakthrough hybrid technology called Differentially Private Generalization, which is an intelligent context based integration of a spectrum of privacy technologies, which offer best of both worlds of balance between utility and privacy protection.

Unlock your data to unlock new business models for your business

With PrivaSapien‚Äôs superior technology of Differentially Private Generalization, businesses can now unlock data based on the context with necessary privacy protection and business utility. We also provide mathematical and graphical proofs of privacy protection as per the recommendations of EU‚Äôs Article 29 ‚ÄĒ Working party recommendations so that businesses can unlock data in silos due to privacy regulatory restrictions.

PrivaSapien’s anonymization frees data from the regulatory burden and hence makes it suitable for data collaboration and cross border transfer. This can unlock huge value for organizations as they can now explore critical insights for businesses from sensitive data without violating privacy of individuals, which was not possible earlier.

January 31, 2024

Privacy Engineering Summit 2024 Mumbai

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