Privacy Terminology Lexicons

Duality’s e-guide to common terminologies and acronyms in the privacy landscape, including terms for cryptography, data privacy, data science, financial, healthcare, and privacy legislation.

Global RegTech Summit 2022- Duality Interview

Duality’s Chief Business Officer, Michael Hughes, discusses key RegTech trends, operational risks face by financial institutions, and how to tackle the roadblocks that are hindering secure data collaboration at the 2022 Global RegTech Summit.

Data and Trust: In Our Opinion – Prof. Shafi Goldwasser and Prof. Daniel Weitzner

Recently, Professor Shafi Goldwasser, a co-founder of Duality Technologies, a Turing award and two-time Gödel Prize winner, interviewed Professor Daniel J. Weitzner, to discuss Internet privacy and public policy. Weitzner is the Director of the MIT Internet Policy Research Initiative, principal research scientist at the Computer Science and Artificial Intelligence Lab CSAIL, and professor of Internet public policy in MIT’s Computer Science Department.

Anomaly Detection: K-Nearest Neighbors

In this blog, we will discuss K-nearest neighbors (KNN), a common technique in anomaly detection. We will then provide an overview of where it intersects with emerging privacy preserving technologies and how it impacts advanced analysis on multiple encrypted datasets.

Privacy-Preserving Machine Learning (PPML)

The answer to the privacy/AI conflict is privacy-preserving machine learning (PPML) – a step-by-step process to allow ML models to be trained without revealing or decrypting the data inputs.

The USA PATRIOT Act: How CPOs Can Adopt a Privacy-First Approach To Secure Data Collaboration

The USA PATRIOT Act Section 314(b) is an important enabler in the fight against financial crime, allowing financial institutions to share vital data with one another for prevention, detection, and investigations. Ronen Cohen, VP of Strategy at Duality Technologies, discusses why financial institutions have struggled to fully utilize this valuable piece of legislation and how privacy-first approaches are paving a new way forward.

The Duality Platform

Today’s data driven enterprise must harness the power of data and realize its value. However, in many cases, organizations are limited due to the complexity of their data footprint, compliance, and privacy regulation factors, as well as competitive, business policy, and budget assignment concerns.

Data and Trust: In Our Opinion – Rina Shainski and Jules Polonetsky

Jules Polonetsky is the CEO of The Future of Privacy Forum (FPF), a think tank based in Washington DC. FPF aims to serve as a platform for policymakers, industry leaders, and academics to come together and discuss emerging technologies and the privacy challenges that they pose.

Data and Trust: In Our Opinion – Rina Shainski and Prof. Shafi Goldwasser

Two Duality Technologies co-founders, Rina Shainski and Shafi Goldwasser, recently met to discuss cryptography. A two-time Gödel Prize and Turing Award winner, Goldwasser tells of her early interest and beginnings in the field. They then examine why the field has become so important in recent years and how Duality facilitates privacy in an increasingly data driven world.

Data Privacy: A Runbook for Engineers

Join us for a riveting discussion about current issues in data privacy and privacy engineering in this fun interview with Nishant Bhajaria, author of the Data Privacy: A Runbook for Engineers and Director of Privacy Engineering at Uber.

Eliminate Data Silos to Unleash the Power of Collaboration

The Duality Platform was developed to solve the security-utility conflict and unleash a new era of data collaboration allowing organizations to reap all the benefits of data sharing and collaboration without ever putting sensitive data at risk.

Leveraging Multisource Health Data to Personalize Health Plans

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.