TL;DR: We allow real-time analysis of sensitive data, providing mathematically guaranteed private-preserving outputs by introducing randomization to the computation process rather than to the data itself.
Differential Privacy (DP) offers the strongest possible privacy protection available today, with a mathematical guarantee to back up each algorithm. Differential privacy is achieved by introducing statistical noise. The noise is significant enough to protect the privacy of any individual in the data, but small enough that it will not impact the accuracy of analytics and machine learning methods applied on the data.
PVML uses proprietary Differential Privacy technology to extract useful insights directly from sensitive datasources. Our algorithms are applied to the computation itself, on-the-fly, so that the outputs are privacy-preserving and can be safely used or shared by the user or third-party.
TL;DR: As opposed to Homomorphic Encryption, Differential Privacy has no overhead in computation and memory cost, and it also guarantees privacy at the output level, preventing reverse engineering and attribute inference attacks.
Homomorphic Encryption allows computation directly on encrypted data, however – it isn’t efficient. Because Homomorphic Encryption comes with a large performance overhead, computations that are already costly to do on unencrypted data probably aren’t feasible on encrypted data. Moreover, although the data is unreadable, the computations performed on it remain the same, including the outputs. When outputs are returned in perfect accuracy, the privacy of individuals in the data cannot be guaranteed, and the dataset remains vulnerable to re-identification attacks where sensitive raw data may be extracted in reverse engineering and attribute inference attacks.
TL;DR: PVML prioritizes applicable algorithmic capabilities, beyond what science can currently provide in the field of Differential Privacy.
PVML incorporates beyond state-of-the-art research objectives along with software engineering and applied machine learning in order to provide the most efficient Differential Privacy algorithms that produce privacy-preserving results with higher accuracy than existing Differential Privacy solutions. Applicability is our first priority, ensuring that our Differential Privacy algorithms can be seamlessly integrated into a wide range of applications and systems, and without changing the methods, tools or languages you use to interact with data. Whether you are in healthcare, finance, telecommunications, or any other industry, our cutting-edge solutions are designed to safeguard sensitive information while maintaining the utility and integrity of your data. Our commitment to applicability extends to easy deployment, scalability, and adaptability, allowing organizations of all sizes to benefit from state-of-the-art privacy protection without compromising performance.
TL;DR: PVML has been verified by legal and technological experts in the security and privacy field, and is trusted by the largest organizations worldwide.
PVML fully meets regulatory and compliance requirements by design. Our architecture never stores or copies any raw data — all processing occurs in real time within the client’s controlled environment, ensuring data residency, sovereignty, and auditability. PVML is SOC 2 certified and its technology and privacy framework have been independently verified by legal and cybersecurity experts. Trusted by leading global organizations, PVML consistently aligns with the highest regulatory and privacy standards.
TL;DR: Yes, anonymization is an outdated technique that leaves expensive data value on the table and fails to guarantee privacy, especially in the current age of AI.
Yes! Even when removing personally identifiable information (PIIs), the resulting records often include unique combinations of variables and features that might be linked to other publicly available information in order to re-identify specific people or leak sensitive information. In practice, as long as useful information about individuals is included in the data, it is vulnerable to re-identification attacks (and therefore, not anonymous).
Moreover, as we transition into an era where data is not only accessed by people but increasingly by advanced AI systems, the risks escalate. AI, being smarter, faster, and exposed to a wealth of information, introduces new challenges to traditional anonymization methods. These intelligent systems can perform intricate attribute inferencing, extracting nuanced insights and patterns that may not be readily apparent to human users. This capability, if exploited by human users, poses significant risks of intentional misuse. Moreover, there’s a potential for unintentional mistakes by AI, leading to inadvertent exposure of sensitive information, further amplifying the challenges in safeguarding data integrity and privacy.
Therefore, the evolving landscape of technology requires a comprehensive approach to anonymization to safeguard against risks posed by both human and AI access. PVML’s data protection technology is grounded in mathematics and engineered for the age of AI, ensuring heightened protection against data vulnerabilities and privacy breaches regardless of whether data is accessed by human users, applications, or AI models.
TL;DR: No.
Your sensitive data stays wherever it is located (on-premise / on-cloud) and our platform does not require any duplication or modification of the data.
TL;DR: We are an infrastructure layer that enhances the AI’s accuracy with RAG infrastructure & permissions.
PVML provides an infrastructure that allows enterprises to integrate with the AI provider while guaranteeing data protection and privacy. Open AI give enterprises the promise that they will not train their models using your data. However, this is only one part of the problem, another part is data protection on the end-user side; how do you guarantee users only see allowed outputs? Masking or creating dedicated data views is not enough to solve this, unless you’re willing to leave valuable data on the table and remain vulnerable to re-identification attacks.
Moreover, PVML provides the RAG infrastructure to connect all the organization’s structured data sources to the LLM model to improve its results with live, real-time context while preserving user permissions.
Read more about the use case of analyzing sensitive data with AI