Information Privacy

While Secure Multiparty Computation (MPC) has long been celebrated for its ability to preserve data privacy, recent research reveals its potential goes far beyond confidentiality. From reducing data inequality to reshaping trust and control in data sharing, MPC is evolving into a broader enabler of ethical and inclusive digital transformation. Drawing on insights from: this

Secure Multiparty Computation (MPC) continues to be one of the most vibrant areas in applied cryptography. As organizations look to collaborate on sensitive data without compromising privacy, the development and refinement of efficient, provably secure MPC protocols has become a priority. This article synthesizes insights from Yehuda Lindell’s overview (Sections 3, 5, and 6) and

As data privacy becomes a top priority in sectors like finance, healthcare, and government, technologies that enable secure collaboration without compromising sensitive information are becoming essential. One such technology is Secure Multiparty Computation (MPC). Drawing from Yehuda Lindell’s foundational article and real-world deployment examples like Inpher, this article provides a thorough introduction to what MPC

As digital systems increasingly rely on collaborative data processing across organizations, the demand for techniques that preserve both security and functionality has grown. One such technique is Multiparty Computation (MPC), a powerful cryptographic tool that enables multiple entities to compute on data without revealing their private inputs. This article introduces MPC, explores its core principles,

As the foundational work in differential privacy, “The Algorithmic Foundations of Differential Privacy” by Cynthia Dwork and Aaron Roth offers not only rigorous theory but also valuable philosophical and practical reflections. In Chapter 13 (pp. 254–259), the authors step back from definitions and theorems to consider the broader implications and future directions of differential privacy.

In today’s hyperconnected world, data privacy is no longer just a technical issue—it’s a societal and legal imperative. In Chapters 15 and 16 of “An Introduction to Data Privacy” by S.A. Vinterbo (IMT4217 Press, 2024), the author delves into the regulatory landscape of privacy and how it intersects with modern data-driven business models. This article

Differential privacy has rapidly evolved from a theoretical concept into a critical tool for real-world data protection, especially in large-scale, public data releases. Its power lies in its ability to protect individual privacy while preserving the utility of aggregated data—a balance that traditional anonymization methods often fail to achieve. In this article, we explore real-world

As machine learning (ML) becomes integral to systems that process vast amounts of personal data—health records, user behavior, financial transactions—privacy concerns are escalating. To address these, researchers Nicolas Papernot and Abhradeep Thakurta (Google Brain) contributed a pivotal guide on the NIST website titled “How to Deploy Machine Learning with Differential Privacy” (2021). This article summarizes

Differential privacy has grown from a theoretical idea into a real-world privacy solution with critical applications across government, tech, and research. In a recent expert interview, Tabitha Ogilvie, a PhD student at Royal Holloway specializing in fully homomorphic encryption (FHE) and differential privacy, shared her insights on how these technologies interact, the current state of