De-Identification 301: Understanding Your Likely Attackers
Data breaches and re-identification attacks compromise the personal privacy of individuals and both are on the rise. Re-identification results when a record is correctly tied to the person behind that data, even if the data was thought to have been made anonymous.
Re-identification attacks occur because an attacker has the skills, resources and the motivation to do so. Motivations can vary but are usually the result of curiosity or a desire for personal gain. Demonstration attacks happen when a researcher or journalist aims to show that a dataset has been insufficiently de-identified and wants to prove that re-identification is possible.
Malicious re-identification attacks have increased 21%. Understanding the motivation behind the three types of re-identification attack means you can better secure your data against them.
Situation: California’s Consumer Privacy Act inspired Comcast to evolve the way in which they protect the privacy of customers who consent to share personal information with them.
Situation: Integrate.ai’s AI-powered tech helps clients improve their online experience by sharing signals about website visitor intent. They wanted to ensure privacy remained fully protected within the machine learning / AI context that produces these signals.
Situation: Novartis’ digital transformation in drug R&D drives their need to maximize value from vast stores of clinical study data for critical internal research enabled by their data42 platform.
Situation: CancerLinQ™, a subsidiary of American Society of Clinical Oncology, is a rapid learning healthcare system that helps oncologists aggregate and analyze data on cancer patients to improve care. To achieve this goal, they must de-identify patient data provided by subscribing practices across the U.S.
Situation: Needed to enable AI-driven product innovation with a defensible governance program for the safe and responsible use
of voice-to-text data under Shrems II.
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