pvml.com
Open in
urlscan Pro
52.28.236.220
Public Scan
Submitted URL: http://pvml.com/
Effective URL: https://pvml.com/
Submission: On February 12 via manual from US — Scanned from DE
Effective URL: https://pvml.com/
Submission: On February 12 via manual from US — Scanned from DE
Form analysis
1 forms found in the DOMPOST https://forms-eu1.hsforms.com/submissions/v3/public/submit/formsnext/multipart/139837585/76fb8b7c-a847-46f2-87dc-fab6fc9c178e
<form id="hsForm_76fb8b7c-a847-46f2-87dc-fab6fc9c178e" method="POST" accept-charset="UTF-8" enctype="multipart/form-data" novalidate=""
action="https://forms-eu1.hsforms.com/submissions/v3/public/submit/formsnext/multipart/139837585/76fb8b7c-a847-46f2-87dc-fab6fc9c178e"
class="hs-form-private hsForm_76fb8b7c-a847-46f2-87dc-fab6fc9c178e hs-form-76fb8b7c-a847-46f2-87dc-fab6fc9c178e hs-form-76fb8b7c-a847-46f2-87dc-fab6fc9c178e_6908e0a8-94dd-4a9c-b2e2-46839fadb96e hs-form stacked"
target="target_iframe_76fb8b7c-a847-46f2-87dc-fab6fc9c178e" data-instance-id="6908e0a8-94dd-4a9c-b2e2-46839fadb96e" data-form-id="76fb8b7c-a847-46f2-87dc-fab6fc9c178e" data-portal-id="139837585"
data-test-id="hsForm_76fb8b7c-a847-46f2-87dc-fab6fc9c178e" autocomplete="off">
<div class="hs_email hs-email hs-fieldtype-text field hs-form-field"><label id="label-email-76fb8b7c-a847-46f2-87dc-fab6fc9c178e" class="" placeholder="Enter your Email" for="email-76fb8b7c-a847-46f2-87dc-fab6fc9c178e"><span>Email</span><span
class="hs-form-required">*</span></label>
<legend class="hs-field-desc" style="display: none;"></legend>
<div class="input"><input id="email-76fb8b7c-a847-46f2-87dc-fab6fc9c178e" name="email" required="" placeholder="" type="email" class="hs-input" inputmode="email" autocomplete="off" value=""></div>
</div>
<div class="hs_submit hs-submit">
<div class="hs-field-desc" style="display: none;"></div>
<div class="actions"><input type="submit" class="hs-button primary large" value="Subscribe" autocomplete="off"></div>
</div><input name="hs_context" type="hidden"
value="{"embedAtTimestamp":"1707756032930","formDefinitionUpdatedAt":"1703685524990","lang":"en","embedType":"REGULAR","renderRawHtml":"true","userAgent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.6167.160 Safari/537.36","pageTitle":"Data Access Platform for the Age of AI | PVML","pageUrl":"https://pvml.com/","isHubSpotCmsGeneratedPage":false,"formTarget":"#hbspt-form-6908e0a8-94dd-4a9c-b2e2-46839fadb96e","rumScriptExecuteTime":384.9000005722046,"rumTotalRequestTime":868.8000001907349,"rumTotalRenderTime":886,"rumServiceResponseTime":483.8999996185303,"rumFormRenderTime":17.199999809265137,"connectionType":"4g","firstContentfulPaint":0,"largestContentfulPaint":0,"locale":"en","timestamp":1707756033047,"originalEmbedContext":{"portalId":"139837585","formId":"76fb8b7c-a847-46f2-87dc-fab6fc9c178e","region":"eu1","target":"#hbspt-form-6908e0a8-94dd-4a9c-b2e2-46839fadb96e","isBuilder":false,"isTestPage":false,"isPreview":false,"css":" ","translations":{"en":{"submitText":"Subscribe"}},"locale":"en","isMobileResponsive":true},"correlationId":"6908e0a8-94dd-4a9c-b2e2-46839fadb96e","renderedFieldsIds":["email"],"captchaStatus":"NOT_APPLICABLE","emailResubscribeStatus":"NOT_APPLICABLE","isInsideCrossOriginFrame":false,"source":"forms-embed-1.4662","sourceName":"forms-embed","sourceVersion":"1.4662","sourceVersionMajor":"1","sourceVersionMinor":"4662","allPageIds":{},"_debug_embedLogLines":[{"clientTimestamp":1707756033025,"level":"INFO","message":"Retrieved customer callbacks used on embed context: [\"onFormReady\"]"},{"clientTimestamp":1707756033026,"level":"INFO","message":"Retrieved pageContext values which may be overriden by the embed context: {\"pageTitle\":\"Data Access Platform for the Age of AI | PVML\",\"pageUrl\":\"https://pvml.com/\",\"userAgent\":\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.6167.160 Safari/537.36\",\"isHubSpotCmsGeneratedPage\":false}"},{"clientTimestamp":1707756033027,"level":"INFO","message":"Retrieved countryCode property from normalized embed definition response: \"DE\""}]}"
autocomplete="off"><iframe name="target_iframe_76fb8b7c-a847-46f2-87dc-fab6fc9c178e" style="display: none;"></iframe>
</form>
Text Content
* Home * About Us * Product * Our Technology * Solutions * Analyze Data with AI * Data Anonymization * Data Monetization * Resources * Blog * Glossary * FAQ’s * Contact Us Book a Demo Book a Demo THE DATA ACCESS PLATFORM ENGINEERED FOR THE AGE OF AI PVML helps connect, provide access, and secure multiple data sources. We enable enterprises to get live insights from sensitive data by combining AI with our data protection technology. Book a Demo Trusted by the most innovative organizations * * * * * * * ANALYZE DATA WITH AI Users can analyze data using AI to replace complex queries with free text. They can export the results, create graphs, or open the underlying queries for further analysis in a SQL notebook. Learn more Your browser does not support the video tag. * NO GREY AREA Users can ask anything they want – no need to think twice if the question is compliant, PVML does this in real time to make sure users only get the results they’re allowed to see. Learn more Your browser does not support the video tag. * ALL-IN-ONE Analyze data from multiple data sources to create unique analytics flows using SQL notebooks, free text chats and python code. Learn more Your browser does not support the video tag. 01 01 03 * * * WHY PVML? Join the paradigm shift in accessing sensitive data. ONE-FITS-ALL COMPLIANCE Eliminate the need for tailored solutions by demonstrating compliance with multiple security frameworks and regulations. Learn more ACCELERATE TIME-TO-INSIGHT Perform real-time, online analytics without worrying about privacy risks. Learn more 3RD PARTY COLLABORATION Enable safe access for third parties without sharing or moving sensitive data. Learn more ENHANCING ACCESS Provide fast and secure access for a broader spectrum of employees. Learn more INTEGRATION WITH AI Use AI to analyze data using free text without sharing sensitive information with AI providers. Learn more LEVERAGE FULL DATA Throw away your anonymization and masking scripts and start harnessing the full data without tagging Personally Identifiable Information (PII). Learn more View all TRUSTED BY Innovative leaders in the data realm, investors, partners and clients. PVML’s unique technology is changing the way companies handle private data. Their concept is simple and intuitive, transformative in value, and encapsulates the complexity under the hood. Gigi Levy-Weiss General Partner at NFX PVML has managed to democratize access to tools that were once considered exclusive only to the largest and most advanced technology companies. Today, PVML tools give every company a capability to leverage sensitive data, redefining the way to navigate use cases such as data anonymization, data sharing, and data monetization. Stan Chudnovsky VP of Messaging, Meta PVML refuses to settle for the status quo; they have a clear vision of a future where data is harnessed responsibly to promote business objectives. Alon Leibovich Managing Director, Intel Ignite PVML’s commitment to responsible data practices aligns perfectly with the demands of my role as a CISO, and their innovative approach positions them as a crucial ally in safeguarding sensitive information and reducing the attack surface. Nir Rothenberg CISO, Rapyd The market is currently missing a comprehensive Differential Privacy solution that can offer more than basic use-cases, and PVML aims to fulfill just that. Ariel Michaeli Head of Israel Investments, Motorola Solutions PVML’s concept allows companies to unlock the full potential of their data, and puts me in a unique position to challenge my executive peers to take a more competitive position on the data we own and be more competitive in the market. Iftach Ian Amit CISO, Investor LATEST BLOG POSTS Explore Our Recent Insights and Updates. * AI NAVIGATING DATA PROTECTION IN THE AGE OF AI AI needs data, while people want privacy. AI-based algorithms or models learn how to yield an output for a given input or query by... 7 min read * Data PrivacyTechnology INTRODUCTION TO PRIVACY ENHANCING TECHNOLOGIES Introduction to Privacy Enhancing Technologies Does data confidentiality or security provide protection for data privacy? Data security plays a crucial role in safeguarding data, but... 6 min read * Data PrivacyTechnology DIFFERENTIAL PRIVACY: WHAT IS ART. 29 WP REALLY SAYING ABOUT DATA ANONYMIZATION? In the ever-expanding digital landscape, where processing personal data is both a valuable resource and a potential threat to privacy, the concepts of data... 10 min read * Data PrivacyTechnology HOW TO LEVERAGE PET FOR DATA PROTECTION AND PRIVACY In today's data-driven world, protecting personal data and privacy has become paramount. With the proliferation of data breaches and the increasing amount of personal... 10 min read * Technology THE MOST COMMON DATA ANONYMIZATION TECHNIQUES What is Data Anonymization? In the age of data-driven decision-making, the value of data is immeasurable. Businesses, healthcare organizations, and governments alike rely on data... 12 min read * SoftwareTechnology TRY IT OUT: INTRODUCING THE FIRST-EVER DIFFERENTIALLY PRIVATE SURVEY GENERATOR “There are three kinds of lies: lies, damned lies, and statistics.” Have you ever found yourself staring at an online poll, wanting to vote but... 5 min read * Technology WHAT’S DIFFERENTIAL PRIVACY? TL;DR? Watch our short explainer video on Differential Privacy! Differential Privacy Differential Privacy is not actually a technology, you can instead view it as a... 3 min read * Data Privacy TOP 4 DATA PRIVACY MISCONCEPTIONS BY CORPORATES Data privacy is no longer “nice to have”, but rather imperative to proper business operation. In this article, we lay out the top data privacy... 4 min read FREQUENTLY ASKED QUESTIONS Everything you need to know. PVML PROVIDES A SECURE FOUNDATION THAT ALLOWS YOU TO PUSH THE BOUNDARIES TL;DR: We allow analytics and ML to be applied on sensitive data, providing mathematically guaranteed private outputs by introducing randomization to the computation. Differential privacy (DP) is a set of systems and practices that help keep the data of individuals safe and private. Differential Privacy 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 offers proprietary Differential Privacy technology to exract useful insights and train AI models using datasets containing sensitive information. Our algorithms are performed on the analysis itself, on-the-fly, so that the outputs are privacy-preserving and can be safely used or shared by the user or third-party. Learn more about how we use Differential Privacy HOW IS DIFFERENTIAL PRIVACY DIFFERENT FROM HOMOMORPHIC ENCRYPTION? 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. Read more about Differential Privacy DOES PVML OFFER UNIQUE DIFFERENTIAL PRIVACY CAPABILITIES? 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. Read more about our Differential Privacy technology HOW DO YOU ADDRESS REGULATORY DEMANDS? TL;DR: PVML has been verified by legal and technological experts in the privacy field. The legislation mandates companies to design their products and processes with privacy in mind, meaning that a company is responsible for ensuring and maintaining the privacy of the personal data it handles. We work alongside a legal team and various security and privacy experts who provide guidance and validation throughout our development process, thereby ensuring that our Differential Privacy algorithms and overall approach maintain individuals’ privacy in accordance with various privacy regulations. Furthermore, we undergo rigorous external audits to ensure that our solution adheres to the highest standards of privacy and security and is SOC2 compliant. Read more about Differential Privacy DO I STILL NEED PVML IF MY DATA DOES NOT CONTAIN ANY IDENTIFIABLE FEATURES? 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. Read more about the downfall of anonymization on our blog DO I NEED TO MOVE MY DATA IN ORDER TO USE PVML'S SOLUTION? 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. Read more about our deployment and architecture PVML. DATA PEACE OF MIND. Experience the freedom of real-time analytics and the power of data sharing, all while ensuring unparalleled privacy. Book a Demo © 2024 PVML All rights reserved. NAVIGATION * Home * About Us * Product * Our Technology * Data Anonymization * Data Monetization * Blog * Glossary * FAQ’s * Contact Us SUBSCRIBE TO OUR NEWSLETTER Email* * Terms of Use * Privacy Policy Connect with us * * Website Design & Development InCreativeWeb.com