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on
1
Intro
2
The Problem
3
Key Point to Take Away
4
The System Model
5
The Contradiction
6
Two Goals
7
Differential Privacy!
8
About That First One...
9
Netflix Data
10
Threat Model
11
Temporal
12
Situational
13
Non-Obvious Relations
14
Adventures with AOL
15
First Aftermath
16
What This Means
17
Tying Votes to People
18
Conclusion
19
A Parting Thought
20
Contact Information
Description:
Explore the complexities of data anonymization in this 23-minute conference talk from USENIX Enigma 2020. Delve into the challenges of protecting anonymized data as Matt Bishop from the University of California, Davis, examines how adversaries can exploit relationships between data fields to reveal hidden information. Learn about the role of external data in uncovering these relationships and approach data anonymization as a risk management problem. Gain insights into differential privacy, analyze real-world examples like the Netflix and AOL data breaches, and understand the implications for data protection strategies. Discover the importance of considering temporal, situational, and non-obvious relations in data anonymization efforts.

How Anonymous Is My Anonymized Data?

USENIX Enigma Conference
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