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What is Differential Privacy

Chapter 10
Big Data Analytics Techniques for Market Intelligence
This technique adds a specific kind of 'noise' or random variation to the data, making identifying individuals in a dataset difficult. It allows the helpful sharing of aggregate information about groups while ensuring the confidentiality of individual data points, balancing data utility with privacy protection.
Published in Chapter:
Suggesting New Techniques and Methods for Big Data Analysis: Privacy-Preserving Data Analysis Techniques
Puneet Gangrade (Fordham University, USA)
Copyright: © 2024 |Pages: 27
DOI: 10.4018/979-8-3693-0413-6.ch010
Abstract
New privacy rules and responsible data use are pushing companies to find clever ways to learn from data without exposing personal details. This chapter explains different techniques that protect privacy while still gaining insights. They provide strong privacy guarantees so organizations can use and share data safely. Real-world examples show how companies in marketing, healthcare, banking, and other industries apply these techniques to drive business value through secure collaboration and accelerated innovation. Recommendations help teams choose and test the right privacy tools for their needs. With the proper privacy toolbox, market intelligence can thrive in an era of ethical data analysis. Organizations that embrace privacy-first practices will gain a competitive advantage and consumer trust. This chapter equips teams to adopt modern privacy-preserving approaches to tap hidden insights in data while respecting user confidentiality.
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Chapter 9
Federated Learning for Privacy Preservation in Healthcare: A Comprehensive Introduction
A privacy-preserving technique involving the addition of noise to individual data points to improve privacy without compromising model accuracy.
Published in Chapter: Federated Learning for Privacy Preservation in Healthcare: A Comprehensive Introduction; From: Federated Learning and Privacy-Preserving in Healthcare AI
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Chapter 3
Web Search Privacy Evaluation Metrics
Differential privacy measures the degree to which individual records in a dataset can be distinguished by an attacker. It is typically measured using metrics such as epsilon, which evaluates the amount of noise that must be added to a dataset to achieve a desired level of privacy.
Published in Chapter: Web Search Privacy Evaluation Metrics; From: Protecting User Privacy in Web Search Utilization
Full Text Chapter Download: US $37.50 Add to Cart
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