Information hiding behind approximate computation

Ye Wang; Qian Xu; Gang Qu; Jian Dong

doi:10.1145/3299874.3319456

Information hiding behind approximate computation

Ye Wang
, Qian Xu
, Gang Qu
, Jian Dong

School of Computer Science and Technology, Harbin Institute of Technology
University of Maryland, College Park

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

There are many interesting advances in approximate computing recently targeting the energy efficiency in system design and execution. The basic idea is to trade computation accuracy for power and energy during all phases of the computation, from data to algorithm and hardware implementation. In this paper, we explore how to utilize approximate computing for security based information hiding. More specifically, we will demonstrate with examples the potential of embedding information in approximate hardware and approximate data, as well as during approximate computation. We analyze both the security vulnerabilities that this may cause and the potential security applications enabled by such information hiding. We argue that information could be hidden behind approximate computation without compromising the computation accuracy or energy efficiency.

Original language	English
Title of host publication	GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI
Publisher	Association for Computing Machinery
Pages	405-410
Number of pages	6
ISBN (Electronic)	9781450362528
DOIs	https://doi.org/10.1145/3299874.3319456
State	Published - 13 May 2019
Externally published	Yes
Event	29th Great Lakes Symposium on VLSI, GLSVLSI 2019 - Tysons Corner, United States Duration: 9 May 2019 → 11 May 2019

Publication series

Name	Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference	29th Great Lakes Symposium on VLSI, GLSVLSI 2019
Country/Territory	United States
City	Tysons Corner
Period	9/05/19 → 11/05/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Approximate computing
Authentication
Information hiding
Machine learning
Security

Access to Document

10.1145/3299874.3319456

Cite this

@inproceedings{42e4b1aad52541f49f43a0f349091c64,

title = "Information hiding behind approximate computation",

abstract = "There are many interesting advances in approximate computing recently targeting the energy efficiency in system design and execution. The basic idea is to trade computation accuracy for power and energy during all phases of the computation, from data to algorithm and hardware implementation. In this paper, we explore how to utilize approximate computing for security based information hiding. More specifically, we will demonstrate with examples the potential of embedding information in approximate hardware and approximate data, as well as during approximate computation. We analyze both the security vulnerabilities that this may cause and the potential security applications enabled by such information hiding. We argue that information could be hidden behind approximate computation without compromising the computation accuracy or energy efficiency.",

keywords = "Approximate computing, Authentication, Information hiding, Machine learning, Security",

author = "Ye Wang and Qian Xu and Gang Qu and Jian Dong",

note = "Publisher Copyright: {\textcopyright} 2019 ACM.; 29th Great Lakes Symposium on VLSI, GLSVLSI 2019 ; Conference date: 09-05-2019 Through 11-05-2019",

year = "2019",

month = may,

day = "13",

doi = "10.1145/3299874.3319456",

language = "英语",

series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",

publisher = "Association for Computing Machinery ",

pages = "405--410",

booktitle = "GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI",

address = "美国",

}

Wang, Y, Xu, Q, Qu, G & Dong, J 2019, Information hiding behind approximate computation. in GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI., 3319456, Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, Association for Computing Machinery , pp. 405-410, 29th Great Lakes Symposium on VLSI, GLSVLSI 2019, Tysons Corner, United States, 9/05/19. https://doi.org/10.1145/3299874.3319456

Information hiding behind approximate computation. / Wang, Ye; Xu, Qian; Qu, Gang et al.
GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI. Association for Computing Machinery , 2019. p. 405-410 3319456 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Information hiding behind approximate computation

AU - Wang, Ye

AU - Xu, Qian

AU - Qu, Gang

AU - Dong, Jian

PY - 2019/5/13

Y1 - 2019/5/13

N2 - There are many interesting advances in approximate computing recently targeting the energy efficiency in system design and execution. The basic idea is to trade computation accuracy for power and energy during all phases of the computation, from data to algorithm and hardware implementation. In this paper, we explore how to utilize approximate computing for security based information hiding. More specifically, we will demonstrate with examples the potential of embedding information in approximate hardware and approximate data, as well as during approximate computation. We analyze both the security vulnerabilities that this may cause and the potential security applications enabled by such information hiding. We argue that information could be hidden behind approximate computation without compromising the computation accuracy or energy efficiency.

AB - There are many interesting advances in approximate computing recently targeting the energy efficiency in system design and execution. The basic idea is to trade computation accuracy for power and energy during all phases of the computation, from data to algorithm and hardware implementation. In this paper, we explore how to utilize approximate computing for security based information hiding. More specifically, we will demonstrate with examples the potential of embedding information in approximate hardware and approximate data, as well as during approximate computation. We analyze both the security vulnerabilities that this may cause and the potential security applications enabled by such information hiding. We argue that information could be hidden behind approximate computation without compromising the computation accuracy or energy efficiency.

KW - Approximate computing

KW - Authentication

KW - Information hiding

KW - Machine learning

KW - Security

UR - https://www.scopus.com/pages/publications/85083247960

U2 - 10.1145/3299874.3319456

DO - 10.1145/3299874.3319456

M3 - 会议稿件

AN - SCOPUS:85083247960

T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

SP - 405

EP - 410

BT - GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI

PB - Association for Computing Machinery

T2 - 29th Great Lakes Symposium on VLSI, GLSVLSI 2019

Y2 - 9 May 2019 through 11 May 2019

ER -

Information hiding behind approximate computation

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this