The Threat of Learning Beyond the Intended Purpose

From Privacy-Preserving Machine Learning by J. Morris Chang, Di Zhuang, and G. Dumindu Samaraweera

This article delves into how Machine Learning algorithms interact with data and the importance of preserving data privacy.

Read it if you’re a machine learning engineer, or a developer building around machine learning.

Machine Learning (ML) can be seen as the capability of an algorithm to mimic intelligent human behavior in terms of performing complex tasks in a way how humans solve problems by looking at the data from different angles and analyzing them across different domains. As we can see, this process of learning is being utilized by various applications in our day-to-day life, from product recommendation systems in online web portals to sophisticated mechanisms of intrusion detection in internet security applications.

USE OF PRIVATE DATA ON THE FLY

In terms of producing high confidence results, machine learning applications require vast amounts of data collected from various sources. The web search queries, browsing history, transaction history of online purchases, movie preferences, individual location check-ins are some of the information that is being collected and stored on a daily basis, most of the time even without being known to the users. Some of this information is private to the individual’s, and somehow are being uploaded…