May 2022 Summaries

Nishant Bhajaria, Director of Data Privacy Engineering at Uber and author of "Data Privacy: A Runbook for Engineers," discusses his career journey and motivation for writing the book, which aims to provide a comprehensive guide for engineers and other stakeholders on privacy and security. His book draws from his personal and professional experiences, aiming to bridge the gap between technologists and consumers by addressing the challenges and responsibilities of big tech. Bhajaria emphasizes the importance of building trustworthy platforms and fostering a new consensus in the tech industry, ensuring that it serves as an ally to all users, not just investors. He highlights the role of storytelling in making complex privacy issues relatable, thereby enhancing understanding and engagement across diverse audiences.

Duality Technologies has been recognized by CB Insights as one of the most promising private artificial intelligence companies, being included in the annual AI 100 ranking. This acknowledgment highlights Duality's leadership in privacy-preserving machine learning, emphasizing its innovative approach to enabling secure data collaboration without compromising privacy or regulatory compliance. The company's platform uses privacy-enhancing technologies to allow secure computations on encrypted data, benefiting highly regulated industries. Duality's selection from over 7,000 companies signifies its potential and impact in the data-driven economy, with notable partners such as DARPA, Intel, and IBM. The AI 100 list, now in its sixth year, spans 13 industries and includes companies that have collectively raised over $12 billion in equity funding since 2017.

Section 314(b) of the USA PATRIOT Act allows financial institutions to share information with one another to identify and report potential money laundering and terrorist activities, offering a safe harbor for participants. A recent roundtable discussion explored the benefits and barriers of this legislation, highlighting its underutilization due to challenges such as resource constraints, prioritization, regulatory clarity, and privacy concerns. While the legislation is seen as a valuable tool for combating financial crimes, institutions are hesitant to fully utilize it due to legal ambiguities and potential reputational risks. The discussion underscored the need for practical solutions, including a privacy-first approach and the use of Privacy Enhancing Technologies (PETs), to facilitate seamless and secure information sharing while maintaining trust and compliance. PETs can help institutions derive insights from encrypted data and share information securely, addressing privacy and efficiency concerns in information sharing efforts.

Trade finance fraud, particularly duplicate trade financing, is a growing concern for financial institutions, exacerbated by the post-COVID-19 supply chain demands and retreat of major banks from trade funding due to fraud risks. High-profile cases, like the $9 billion scandal involving Hin Leong Trading, highlight the vulnerabilities in trade finance, where perpetrators secure multiple financings for the same transaction due to antiquated processes, lack of standardization, and limited technology use for fraud prevention. Privacy regulations and competitive concerns pose challenges for cross-border data sharing needed to prevent such fraud, but advancements in modern IT, data science, and cryptography offer promising solutions. Technologies like Privacy Enhancing Technologies (PETs), federated learning, and homomorphic encryption can help financial institutions share data while preserving privacy and compliance, ultimately enabling them to adopt a strategic, collaborative approach to fraud prevention. By leveraging AI and data modeling, financial institutions can enhance their predictive capabilities and support decision-making to mitigate risks while contributing to the evolution of financial standards for privacy-preserving data collaboration.

Data has become an invaluable asset in today's economy, driving business innovation and opportunities across various industries. Tech giants and data-driven enterprises leverage advanced computational tools to predict trends and enhance decision-making but often require collaboration with third parties to fill data gaps. This collaboration, particularly in sectors like pharmaceuticals, cloud services, financial services, and post-merger scenarios, depends heavily on trust and legal frameworks, which can be costly and time-consuming. Privacy-enhancing technologies (PETs) present a solution by allowing data to remain encrypted during collaboration, reducing legal and security risks while ensuring compliance with data protection regulations. This approach simplifies legal complexities and facilitates faster and more secure data collaborations, minimizing the risk of data breaches and enhancing the potential for uncovering new business insights.