As microservices architectures have become increasingly prevalent in modern software systems, they’ve brought both tremendous benefits and significant challenges. One of the most pressing challenges has been maintaining performance at scale while dealing with complex service dependencies and network communication overhead.

In contemporary technology environments, organisations are increasingly challenged with the complexities of privacy engineering. The evolving data governance and regulatory ecosystems demand not only technical ingenuity but also a deep understanding of legal frameworks and organizational dynamics.

At the heart of privacy lies the principle of purpose limitation, dictating that data should only be processed for explicitly stated purposes. This principle presents a considerable challenge, especially for organisations operating at the scale of Meta, which handles vast amounts of data from billions of users.

Recently I migrated this website from Ghost to Hugo. This site is now generated by Hugo, stored by Github, deployed on Cloudflare Pages, and content managed via Decap CMS. Hugo, Decap CMS, Cloudflare Pages, and GitHub together create a powerful and efficient stack for building, managing, and deploying static websites.

Differential Privacy is a powerful framework for ensuring privacy in data analysis by adding controlled noise to computations. Its mathematical foundation guarantees that the presence or absence of any individual’s data in a dataset does not significantly affect the outcome of an analysis. Here are six key equations that capture the essence of differential privacy and its mechanisms, along with references to their origins and explanations.

Since 2009, I have published over 125 articles on this blog, creating a space where my work intersects with the rigor of academic research and the applied challenges of industry. This platform has grown beyond a personal archive. It has become a resource for researchers, industry practitioners, and students alike. It currently attracts more than 18k monthly visitor from across the globe.

Privacy in data systems has traditionally focused on protecting sensitive information as it enters a system - what we call input privacy. However, as systems become more complex and capable of inferring sensitive information from seemingly harmless data, the importance of output privacy has gained significant attention. Let’s explore these two crucial aspects of privacy protection and understand how different techniques address them.

Secure multi-party computation (SMPC) enables organisations to collaborate on sensitive data analysis without directly sharing raw information. However, seemingly harmless aggregate outputs, particularly private set intersection (PSI), can leak individual-level information when analysed strategically over time.

Multi-touch attribution is considered as holy grail in advertising industry. As advertisers are targeting users with multiple advertisements across different platforms and publishers, understanding how each of these touch points contributes to conversion is crucial—but this understanding has traditionally come at the cost of user privacy.

Safeguarding individual privacy inherently means data minimisation i.e. limiting the collection and disposal of data. This principle has been a cornerstone of privacy advocacy and is even enshrined in regulations like the EU’s General Data Protection Regulation (GDPR). However, a research published by Ponte et. al is challenging this fundamental assumption, introducing what they call the “Where’s Waldo effect.