Read this informative and creative take from our friends at Conversational Geek to see how PETs have evolved to revolutionize data sharing and learn how to get started with PETs and incorporate them into your IT processes.
Today, healthcare data is more important than ever – and so is keeping patient privacy intact. How do we solve the conflict between the need for better predictions, insights, and analysis on the wealth of Real-World healthcare data and the need to keep patient data private? In this brief talk, Dr. Alon Kaufman, CEO and co-founder of Duality Technologies, dives into how Privacy Enhancing Technologies (PETs) are allowing companies to use Real-World Evidence (RWE) to produce better treatments and prevention tactics.
Dana Farber research project uses the Duality platform for GWAS studies.
NTT DATA sought Duality’s platform to support two company-wide initiatives that would empower them to enrich their overall customers’ and partners’ experience by enabling secure data collaboration across multisource data sets. The first initiative was focused on securely analyzing highly sensitive and strictly regulated health data across multiple institutions that would allow NTT DATA Scientists to analyze and learn from previously inaccessible data to make recommendations on personalized health plans.
Join Marcelo Blatt, VP Data Science and Ronen Cohen VP of Strategy, at Duality for a first hand walk through of a real world business scenario that expertly combines cryptography and data science to perform queries and statistical analysis on large, disparate datasets, all under the veil of encryption.
Join Duality CEO and Co-founder, Dr. Alon Kaufman, to discuss the role that science is taking in privacy enabled data collaboration, the varied PETs that organizations are leaning on, and how to unleash the value of your sensitive data by adopting a privacy enhanced data collaboration strategy.
In this hands-on technical webinar, you will navigate the data sharing landscape and drive alongside two of our most experienced solution architects to understand how they initiate a Duality PoC and the challenges that they commonly uncover.
In today’s fragmented data sharing landscape, which is riddled with policy and regulations, it’s time to empower organizations to extract value from their data, capitalize on the insights it drives, and evolve their overall data collaboration ability all while protecting the most sensitive information contained within their data.
As our global data footprint continues to grow, so does the need for adopting technologies that enable you to initiate privacy preserving analytics that drive value and actionable outcomes.
Rina Shainski, our Chairwoman and Co-Founder, speaks at the German-Israeli Health Forum of Artificial Intelligence about the value of secure data collaboration for RWE studies and more.
Dr. Alexander Gusev from the Dana Farber Research Institute discusses how PETs are being used today to build models predicting who is at higher risk for serious diseases.
Learn about how Privacy-Enhancing Technologies (PETs) cut the red tape from RWE studies and enable medical institutions to securely collaborate on sensitive data.
Yuriy Polyakov, PALISADE co-founder and project lead, describes best practices for building efficient homomorphic encryption solutions based on approximate homomorphic encryption. These guidelines are applied to the problem of building efficient privacy-preserving Genome-Wide Association Studies (GWAS).
PALISADE Webinar #7A-Secure Large-Scale Genome-Wide Association Studies using Homomorphic Encryption
Alexander Gusev, assistant professor at Harvard Medical School, introduces the application of homomorphic encryption for privacy-preserving genomic analysis, focusing on the application of PALISADE for Genome-Wide Association Studies (GWAS) workloads used in practice.
Prof. Shafi Goldwasser speaks on how cryptographic models and tools can and should play a role in ensuring the trustworthiness of AI and machine learning and address problems such as privacy of training input, model verification and robustness against adversarial examples.
Dr. Alon Kaufman explores the healthcare potential for PPML on encrypted data at the Tel Aviv University AI Week 2019.