Title
Performance evaluation of Attribute-Based Encryption: Toward data privacy in the IoT
Abstract
With the ever increasing number of connected devices and the over abundance of data generated by these devices, data privacy has become a critical concern in the Internet of Things (IoT). One promising privacy-preservation approach is Attribute-Based Encryption (ABE), a public key encryption scheme that enables fine-grained access control, scalable key management and flexible data distribution. This paper presents an in-depth performance evaluation of ABE that focuses on execution time, data and network overhead, energy consumption, and CPU and memory usage. We evaluate two major types of ABE, Key-Policy Attribute-Based Encryption (KP-ABE) and Ciphertext-Policy Attribute-Based Encryption (CP-ABE), on different classes of mobile devices including a laptop and a smartphone. To the best of our knowledge, this is the first comprehensive study of ABE dedicated solely to its performance. Our results provide insights into important practical issues of ABE, including what computing resources ABE requires in heterogeneous environments, at what cost ABE offers benefits, and under what situations ABE is best suited for use in the IoT.
Year
DOI
Venue
2014
10.1109/ICC.2014.6883405
ICC
Keywords
Field
DocType
mobile devices,execution time,flexible data distribution,in-depth performance evaluation,memory usage,power consumption,data privacy,cp-abe,privacy-preservation approach,key-policy attribute-based encryption,iot,smartphone,ciphertext-policy attribute-based encryption,network overhead,laptop,scalable key management,public key cryptography,energy consumption,public key encryption,data overhead,smart phones,cpu,laptop computers,kp-abe,heterogeneous environments,fine-grained access control,internet of things,access control,encryption
Key management,Client-side encryption,Computer security,Computer science,Attribute-based encryption,Computer network,Encryption,40-bit encryption,Information privacy,Public-key cryptography,Privacy software
Conference
ISSN
Citations 
PageRank 
1550-3607
25
1.13
References 
Authors
12
4
Name
Order
Citations
PageRank
Xinlei Wang122816.47
Jianqing Zhang2423.32
Eve M. Schooler32695314.25
Mihaela Ion419614.00