Myths and fallacies of "Personally Identifiable Information"

Authors:
Arvind Narayanan;Vitaly Shmatikov
Affiliations:
Stanford University;University of Texas at Austin
Venue:
Communications of the ACM
Year:
2010

Citing 4
Cited 13

Achieving k-anonymity privacy protection using generalization and suppression

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Robust De-anonymization of Large Sparse Datasets

SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
De-anonymizing Social Networks

SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
A firm foundation for private data analysis

Communications of the ACM

PETs and their users: a critical review of the potentials and limitations of the privacy as confidentiality paradigm

Identity in the Information Society
Developing foundations for accountability systems: informational norms and context-sensitive judgments

Proceedings of the 2010 Workshop on Governance of Technology, Information and Policies
Protecting privacy in data release

Foundations of security analysis and design VI
Legal concepts meet technology: a 50-state survey of privacy laws

Proceedings of the 2011 Workshop on Governance of Technology, Information, and Policies
Fast track article: Balancing behavioral privacy and information utility in sensory data flows

Pervasive and Mobile Computing
Provable de-anonymization of large datasets with sparse dimensions

POST'12 Proceedings of the First international conference on Principles of Security and Trust
Research Note---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation

Information Systems Research
Theoretical Results on De-Anonymization via Linkage Attacks

Transactions on Data Privacy
A PLA-based privacy-enhancing user modeling framework and its evaluation

User Modeling and User-Adapted Interaction
What's in a name?: an unsupervised approach to link users across communities

Proceedings of the sixth ACM international conference on Web search and data mining
Efficient discovery of de-identification policy options through a risk-utility frontier

Proceedings of the third ACM conference on Data and application security and privacy
A framework for context-aware privacy of sensor data on mobile systems

Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
MoodScope: building a mood sensor from smartphone usage patterns

Proceeding of the 11th annual international conference on Mobile systems, applications, and services

Quantified Score

Hi-index	48.22

Visualization

Abstract

Developing effective privacy protection technologies is a critical challenge for security and privacy research as the amount and variety of data collected about individuals increase exponentially.