On Privacy Preserving Partial Image Sharing

Jianping He; Bin Liu; Xuan Bao; Hongxia Jin; George Kesidis

doi:10.1109/ICDCS.2015.95

On Privacy Preserving Partial Image Sharing

Jianping He, Bin Liu, Xuan Bao, Hongxia Jin, George Kesidis

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

2 Scopus citations

Abstract

Sharing photos through Online Social Networks becomes an increasingly popular fashion. However, users' privacy may be at stake when sensitive photos are shared improperly. This paper presents a dynamic privacy protection technique (named PuPPIeS) for image data where the data owner stipulates small private regions for sensitive objects (faces, SSN numbers, etc.) of a photo/image and sets different sharing policies for these partial regions with respect to different individuals. PuPPIeS is based on optimized reversible matrix perturbation of compressed image data. Hence it can naturally support frequently used image transformations. Our experiments show that our solution is effective for privacy protection and incurs only a small overhead for partial image sharing.

Original language	English (US)
Title of host publication	Proceedings - 2015 IEEE 35th International Conference on Distributed Computing Systems, ICDCS 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	758-759
Number of pages	2
ISBN (Electronic)	9781467372145
DOIs	https://doi.org/10.1109/ICDCS.2015.95
State	Published - Jul 22 2015
Event	35th IEEE International Conference on Distributed Computing Systems, ICDCS 2015 - Columbus, United States Duration: Jun 29 2015 → Jul 2 2015

Publication series

Name	Proceedings - International Conference on Distributed Computing Systems
Volume	2015-July

Other

Other	35th IEEE International Conference on Distributed Computing Systems, ICDCS 2015
Country/Territory	United States
City	Columbus
Period	6/29/15 → 7/2/15

All Science Journal Classification (ASJC) codes

Software
Hardware and Architecture
Computer Networks and Communications

Access to Document

10.1109/ICDCS.2015.95

Cite this

He, J., Liu, B., Bao, X., Jin, H., & Kesidis, G. (2015). On Privacy Preserving Partial Image Sharing. In Proceedings - 2015 IEEE 35th International Conference on Distributed Computing Systems, ICDCS 2015 (pp. 758-759). Article 7164973 (Proceedings - International Conference on Distributed Computing Systems; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDCS.2015.95

@inproceedings{4d22bd89600e4dc09f4d3460ec9b89d8,

title = "On Privacy Preserving Partial Image Sharing",

abstract = "Sharing photos through Online Social Networks becomes an increasingly popular fashion. However, users' privacy may be at stake when sensitive photos are shared improperly. This paper presents a dynamic privacy protection technique (named PuPPIeS) for image data where the data owner stipulates small private regions for sensitive objects (faces, SSN numbers, etc.) of a photo/image and sets different sharing policies for these partial regions with respect to different individuals. PuPPIeS is based on optimized reversible matrix perturbation of compressed image data. Hence it can naturally support frequently used image transformations. Our experiments show that our solution is effective for privacy protection and incurs only a small overhead for partial image sharing.",

author = "Jianping He and Bin Liu and Xuan Bao and Hongxia Jin and George Kesidis",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 35th IEEE International Conference on Distributed Computing Systems, ICDCS 2015 ; Conference date: 29-06-2015 Through 02-07-2015",

year = "2015",

month = jul,

day = "22",

doi = "10.1109/ICDCS.2015.95",

language = "English (US)",

series = "Proceedings - International Conference on Distributed Computing Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings - 2015 IEEE 35th International Conference on Distributed Computing Systems, ICDCS 2015",

address = "United States",

}

He, J, Liu, B, Bao, X, Jin, H & Kesidis, G 2015, On Privacy Preserving Partial Image Sharing. in Proceedings - 2015 IEEE 35th International Conference on Distributed Computing Systems, ICDCS 2015., 7164973, Proceedings - International Conference on Distributed Computing Systems, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 758-759, 35th IEEE International Conference on Distributed Computing Systems, ICDCS 2015, Columbus, United States, 6/29/15. https://doi.org/10.1109/ICDCS.2015.95

On Privacy Preserving Partial Image Sharing. / He, Jianping; Liu, Bin; Bao, Xuan et al.
Proceedings - 2015 IEEE 35th International Conference on Distributed Computing Systems, ICDCS 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 758-759 7164973 (Proceedings - International Conference on Distributed Computing Systems; Vol. 2015-July).

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

TY - GEN

T1 - On Privacy Preserving Partial Image Sharing

AU - He, Jianping

AU - Liu, Bin

AU - Bao, Xuan

AU - Jin, Hongxia

AU - Kesidis, George

PY - 2015/7/22

Y1 - 2015/7/22

N2 - Sharing photos through Online Social Networks becomes an increasingly popular fashion. However, users' privacy may be at stake when sensitive photos are shared improperly. This paper presents a dynamic privacy protection technique (named PuPPIeS) for image data where the data owner stipulates small private regions for sensitive objects (faces, SSN numbers, etc.) of a photo/image and sets different sharing policies for these partial regions with respect to different individuals. PuPPIeS is based on optimized reversible matrix perturbation of compressed image data. Hence it can naturally support frequently used image transformations. Our experiments show that our solution is effective for privacy protection and incurs only a small overhead for partial image sharing.

AB - Sharing photos through Online Social Networks becomes an increasingly popular fashion. However, users' privacy may be at stake when sensitive photos are shared improperly. This paper presents a dynamic privacy protection technique (named PuPPIeS) for image data where the data owner stipulates small private regions for sensitive objects (faces, SSN numbers, etc.) of a photo/image and sets different sharing policies for these partial regions with respect to different individuals. PuPPIeS is based on optimized reversible matrix perturbation of compressed image data. Hence it can naturally support frequently used image transformations. Our experiments show that our solution is effective for privacy protection and incurs only a small overhead for partial image sharing.

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M3 - Conference contribution

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BT - Proceedings - 2015 IEEE 35th International Conference on Distributed Computing Systems, ICDCS 2015

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

On Privacy Preserving Partial Image Sharing

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