De-identification for privacy protection in multimedia content: A survey

被引:123
作者
Ribaric, Slobodan [1 ]
Ariyaeeinia, Aladdin [2 ]
Pavesic, Nikola [3 ]
机构
[1] Univ Zagreb, Fac Elect Engn & Comp, Zagreb, Croatia
[2] Univ Hertfordshire, Hatfield, Herts, England
[3] Univ Ljubljana, Fac Elect Engn, Ljubljana, Slovenia
关键词
Privacy; Multimedia; De-identification; Biometric identifiers; Soft biometric identifiers; Non-biometric identifiers; IRIS RECOGNITION; REAL-TIME; FINGERPRINT PATTERNS; SOFT BIOMETRICS; FACE; TRANSFORMATION; SURVEILLANCE; EXTRACTION; NETWORKS; SECURITY;
D O I
10.1016/j.image.2016.05.020
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio video surveillance. Each of these can potentially provide the means for privacy intrusion. De identification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:131 / 151
页数:21
相关论文
共 192 条
[1]   Wireless Smart Camera Networks for the Surveillance of Public Spaces [J].
Abas, Kevin ;
Porto, Caio ;
Obraczka, Katia .
COMPUTER, 2014, 47 (05) :37-44
[2]   A Survey on Ear Biometrics [J].
Abaza, Ayman ;
Ross, Arun ;
Hebert, Christina ;
Harrison, Mary Ann F. ;
Nixon, Mark S. .
ACM COMPUTING SURVEYS, 2013, 45 (02)
[3]  
Abe M., 1988, ICASSP 88: 1988 International Conference on Acoustics, Speech, and Signal Processing (Cat. No.88CH2561-9), P655, DOI 10.1109/ICASSP.1988.196671
[4]   Automatic eye-level height system for face and iris recognition systems [J].
Abiantun, R ;
Savvides, M ;
Khosla, PK .
Fourth IEEE Workshop on Automatic Identification Advanced Technologies, Proceedings, 2005, :155-159
[5]   Matching and reterieval of tattoo images: Active contour CBIR and glocal image features [J].
Acton, Scott T. ;
Rossi, Adam .
2008 IEEE SOUTHWEST SYMPOSIUM ON IMAGE ANALYSIS & INTERPRETATION, 2008, :21-+
[6]   Person De-Identification in Videos [J].
Agrawal, Prachi ;
Narayanan, P. J. .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2011, 21 (03) :299-310
[7]  
Angwin J., 2015, DRAGNET NATION
[8]  
[Anonymous], TOOLS DE IDENTIFICAT
[9]  
[Anonymous], EFFECTIVE SURVEILLAN
[10]  
[Anonymous], 2014, LECT NOTES COMPUT