Privacy engineering applies engineering principles to protect data privacy in systems and products. It involves minimizing data collection, implementing safeguards, and ensuring compliance with privacy laws. Engineers create technical solutions that incorporate privacy-enhancing technologies and follow principles like data minimization. The aim is to protect personal information while maintaining functionality and innovation in digital products.
In last post we covered, Privacy Preserving Measurement (PPM) and discussed how Distributed Aggregation Protocol (DAP) works. Today, we'll explore how to implement a simplified version of the DAP using Python with Prio3 as our Verifiable Distributed Aggregation Function (VDAF). This implementation will...
In 1982, Andrew Yao proposed the Millionaire Problem which discusses how two millionaires can learn who is richest one without disclosing their actual wealth. They solve this problem by comparing their wealth using secure two party computation to ensure that they learn only the richest...
The promise of differential privacy is compelling. It offers a rigorous, provable guarantee of individual privacy, even in the face of arbitrary background knowledge. Rather than relying on anonymization techniques that can often be defeated, differential privacy works by injecting carefully calibrated noise into computations....
Differential Privacy (DP) is a mathematical framework that protects individual privacy in data analysis while allowing useful insights to be extracted. It works by adding carefully calibrated noise to data or query results, ensuring that including or excluding any single individual's data doesn&...
Personal data processing forms the backbone of many big tech service providers. Tech giants like Netflix, Meta, and Amazon employ intricate networks of microservices to automatically process user data, creating complex data flows that span multiple layers of computation. Privacy concerns and regulatory frameworks like...
