학술논문

Anon-Pass: Practical Anonymous Subscriptions
Document Type
Periodical
Source
IEEE Security & Privacy IEEE Secur. Privacy Security & Privacy, IEEE. 12(3):20-27 Jun, 2014
Subject
Computing and Processing
Aerospace
Bioengineering
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Communication, Networking and Broadcast Technologies
Photonics and Electrooptics
Power, Energy and Industry Applications
Publishing
Authentication
Protocols
Subscriptions
Cryptography
Servers
Logic gates
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Language
ISSN
1540-7993
1558-4046
Abstract
Electronic subscriptions are widespread and quickly becoming the dominant mode of access for services like music and video streaming, news, and academic articles. Although electronic subscriptions are convenient for users, they reveal a lot of information, ranging from personal preferences to geographic movements. Many users want electronic services, but they also want privacy. Simply anonymizing data doesn’t always protect users’ privacy. Indeed, multiple anonymized datasets released for research purposes, including the AOL search dataset1 and the Netflix Prize dataset,2 have been partially deanonymized through correlation or by understanding of the semantics of the released data.