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Privacy Intelligence: A Survey on Image Privacy in Online Social Networks [article]

Chi Liu, Tianqing Zhu, Jun Zhang, Wanlei Zhou
2021 arXiv   pre-print

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2020 pre-print arXiv:2008.12199v1 fatcat:htniak3pangmtpixwdgq5n3m34
To fill the gap, we contribute a survey of "privacy intelligence" that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective.  ...  Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion.  ...  To alleviate this drawback, Fan et al. [126] proposed a visual obfuscation solution based on metric privacy [127] which was generalized from differential privacy [128] , [129] .  ... 
arXiv:2008.12199v2 fatcat:vxv6rsnyavesjiw2bbnc4jzsiy
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Chi Liu, Tianqing Zhu, Jun Zhang, Wanlei Zhou. "Privacy Intelligence: A Survey on Image Privacy in Online Social Networks." arXiv (2021)

Advances in privacy-preserving computing

Kaiping Xue, Zhe Liu, Haojin Zhu, Miao Pan, David S. L. Wei
2021 Peer-to-Peer Networking and Applications  

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The twenty-third article by William Croft et al. on 'Obfuscation of Images via Differential Privacy: From Facial Images to General Images' develops a framework and derived the configuration of Laplace  ...  mechanism through which a formal differentially private guarantee for the obfuscation of facial images in generative machine learning models can be obtained.  ...  Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. (2002) Professor and then a Full Professor). Dr.  ... 
doi:10.1007/s12083-021-01110-9 fatcat:o5vvf6ezcna2pc32g6oapioalu
Citation

Kaiping Xue, Zhe Liu, Haojin Zhu, Miao Pan, David S. L. Wei. "Advances in privacy-preserving computing." Peer-to-Peer Networking and Applications (2021)

AnonymousNet: Natural Face De-Identification with Measurable Privacy [article]

Tao Li, Lei Lin
2019 arXiv   pre-print

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With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged.  ...  Not only do we achieve the state-of-the-arts in terms of image quality and attribute prediction accuracy, we are also the first to show that facial privacy is measurable, can be factorized, and accordingly  ...  Acknowledgment The authors especially thank Professor Chris Clifton for insightful discussions in differential privacy and privacy metrics in the context of facial images.  ... 
arXiv:1904.12620v1 fatcat:545gr4nvr5cw5hml5feubxzbcm
Citation

Tao Li, Lei Lin. "AnonymousNet: Natural Face De-Identification with Measurable Privacy." arXiv (2019)

AnonymousNet: Natural Face De-Identification With Measurable Privacy

Tao Li, Lei Lin
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  

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With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged.  ...  Not only do we achieve the state-of-the-arts in terms of image quality and attribute prediction accuracy, we are also the first to show that facial privacy is measurable, can be factorized, and accordingly  ...  Acknowledgment The authors especially thank Professor Chris Clifton for insightful discussions in differential privacy and privacy metrics in the context of facial images.  ... 
doi:10.1109/cvprw.2019.00013 dblp:conf/cvpr/LiL19b fatcat:hmvympue5ve3jba4vgxvxtyvde
Citation

Tao Li, Lei Lin. "AnonymousNet: Natural Face De-Identification With Measurable Privacy." 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019) 56-65

Privacy-Preserving Portrait Matting [article]

Jizhizi Li, Sihan Ma, Jing Zhang, Dacheng Tao
2021 arXiv   pre-print

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https://web.archive.org/web/20210802083628/https://arxiv.org/pdf/2104.14222v2.pdf

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2021 pre-print
arXiv:2104.14222v1 fatcat:einaf7qse5emfbioramw426bca
To fill the gap, we present P3M-10k in this paper, which is the first large-scale anonymized benchmark for Privacy-Preserving Portrait Matting.  ...  P3M-10k consists of 10,000 high-resolution face-blurred portrait images along with high-quality alpha mattes.  ...  Specifically, we use face obfuscation as the privacy-preserving strategy to anonymize the identities of all images.  ... 
arXiv:2104.14222v2 fatcat:dnyajp3gmbgbxibbyn2ozs7siu
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Jizhizi Li, Sihan Ma, Jing Zhang, Dacheng Tao. "Privacy-Preserving Portrait Matting." arXiv (2021)

Privacy–Enhancing Face Biometrics: A Comprehensive Survey

Blaz Meden, Peter Rot, Philipp Terhorst, Naser Damer, Arjan Kuijper, Walter J. Scheirer, Arun Ross, Peter Peer, Vitomir Struc
2021 IEEE Transactions on Information Forensics and Security  

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For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage.  ...  These efforts have resulted in a multitude of privacy-enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative  ...  Content may change prior to final publication.  ... 
doi:10.1109/tifs.2021.3096024 fatcat:z5kvij6g7vgx3b24narxdyp2py
Citation

Blaz Meden, Peter Rot, Philipp Terhorst, Naser Damer, Arjan Kuijper, Walter J. Scheirer, Arun Ross, Peter Peer, Vitomir Struc. "Privacy–Enhancing Face Biometrics: A Comprehensive Survey." IEEE Transactions on Information Forensics and Security (2021) 1-1

Olympus: Sensor Privacy through Utility Aware Obfuscation

Nisarg Raval, Ashwin Machanavajjhala, Jerry Pan
2019 Proceedings on Privacy Enhancing Technologies  

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Personal data garnered from various sensors are often offloaded by applications to the cloud for analytics. This leads to a potential risk of disclosing private user information.  ...  Olympus achieves privacy by designing a utility aware obfuscation mechanism, where privacy and utility requirements are modeled as adversarial networks.  ...  To generate different degrees of obfuscation, we vary the kernel (cell) size from 3 to 50 in the case of Blur (Mosaic) method, and vary λ from 0 to 1 in the case of AdvRep and Olympus.  ... 
doi:10.2478/popets-2019-0002 dblp:journals/popets/RavalMP19 fatcat:xpezsfu7izbrli6evpoxyty4tm
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Nisarg Raval, Ashwin Machanavajjhala, Jerry Pan. "Olympus: Sensor Privacy through Utility Aware Obfuscation." Proceedings on Privacy Enhancing Technologies 2019.1 (2019) 5-25

Privacy Adversarial Network

Sicong Liu, Junzhao Du, Anshumali Shrivastava, Lin Zhong
2019 Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies  

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2020 pre-print arXiv:2006.06535v1 fatcat:lifmbao64vcczpf43xfw5n5rp4
We design a representation encoder that generates the feature representations to optimize against the privacy disclosure risk of sensitive information (a measure of privacy) by the privacy adversaries,  ...  Such a service requires a user to send data, e.g. image, voice and video, to the provider, which presents a serious challenge to user privacy.  ...  DESIGN OF PAN To find a good, hopefully Pareto-optimal tradeoff between utility and privacy, we design PAN to learn an Encoder E(·) via a careful combination of discriminative, generative, and adversarial  ... 
doi:10.1145/3369816 fatcat:qm7xc7y7qrgk5iobd33uzyts6y
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Sicong Liu, Junzhao Du, Anshumali Shrivastava, Lin Zhong. "Privacy Adversarial Network." Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies 3.4 (2019) 1-18

Federated Generative Privacy [article]

Aleksei Triastcyn, Boi Faltings
2019 arXiv   pre-print

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We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw privacy-preserving artificial data samples and empirically assess the risk of information  ...  Our experiments show that FedGP is able to generate labelled data of high quality to successfully train and validate supervised models.  ...  CelebA is a facial attributes dataset with 202599 images, each of which we crop to 128x128 and then downscale to 48x48.  ... 
arXiv:1910.08385v1 fatcat:tyuno5mn5vhwhionuwh44r5wja
Citation

Aleksei Triastcyn, Boi Faltings. "Federated Generative Privacy." arXiv (2019)

Generative Adversarial Privacy [article]

Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal
2019 arXiv   pre-print

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2018 pre-print arXiv:1807.05306v1 fatcat:2u3v5adhcfc5xjoeworvgvxew4

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2019 pre-print
arXiv:1807.05306v2 fatcat:i3jizbff7fhhvfy5pkr3jrv2z4
Inspired by recent advancements in generative adversarial networks (GANs), GAP allows the data holder to learn the privatization mechanism directly from the data.  ...  We present a data-driven framework called generative adversarial privacy (GAP).  ...  Context-free privacy. One of the most popular context-free notions of privacy is differential privacy (DP) [7] .  ... 
arXiv:1807.05306v3 fatcat:pmddbotq4jccpkij4mezdt7cyq
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Chong Huang, Peter Kairouz, Xiao Chen, Lalitha Sankar, Ram Rajagopal. "Generative Adversarial Privacy." arXiv (2019)

Privacy in Deep Learning: A Survey [article]

Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh
2020 arXiv   pre-print

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https://web.archive.org/web/20201111022323/https://arxiv.org/pdf/2004.12254v5.pdf

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2020 pre-print
arXiv:2004.12254v3 fatcat:i6dsgqfp2jdrdknwnkfai6agta
2020 pre-print
arXiv:2004.12254v4 fatcat:hvvy7wqydjd5ni7lppxogqcziu
2020 pre-print
arXiv:2004.12254v2 fatcat:o77ckubymjdzjpbu4slerebxju
2020 pre-print
arXiv:2004.12254v1 fatcat:vysydl6ceff2hfqndsttpcxvn4
In this survey, we review the privacy concerns brought by deep learning, and the mitigating techniques introduced to tackle these issues.  ...  The ever-growing advances of deep learning in many areas including vision, recommendation systems, natural language processing, etc., have led to the adoption of Deep Neural Networks (DNNs) in production  ...  A generalized version of differential privacy called Pufferfish was proposed by [76] .  ... 
arXiv:2004.12254v5 fatcat:4w63htwzafhxxel2oq3z3pwwya
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Fatemehsadat Mireshghallah, Mohammadkazem Taram, Praneeth Vepakomma, Abhishek Singh, Ramesh Raskar, Hadi Esmaeilzadeh. "Privacy in Deep Learning: A Survey." arXiv (2020)

State of the art in privacy preservation in video data

Slavisa Aleksic, Liane Colonna, Carina Dantas, Anton Fedosov, Francisco Florez-Revuelta, Eduard Fosch-Villaronga, Aleksandar Jevremovic, Hajer Gahbiche Msakniç, Siddharth Ravi, Blerim Rexha, Aurelia Tamò-Larrieux
2022 Zenodo  

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Thus, AAL solutions must consider privacy-by-design methodologies in order to protect the fundamental rights of those being monitored.  ...  The General Data Protection Regulation (GDPR) establishes the obligation for technologies to meet the principles of data protection by design and by default.  ...  Then, after explaining the original ideas from pioneers in the field of privacy, we will explain how such an abstract concept made its way to the law via the General Data Protection Regulation (GDPR).  ... 
doi:10.5281/zenodo.6806207 fatcat:lffai2dlvzaxberfqav3xjeani
Open Access
Citation

Slavisa Aleksic, Liane Colonna, Carina Dantas, Anton Fedosov, Francisco Florez-Revuelta, Eduard Fosch-Villaronga, Aleksandar Jevremovic, Hajer Gahbiche Msakniç, Siddharth Ravi, Blerim Rexha, Aurelia Tamò-Larrieux. "State of the art in privacy preservation in video data." Zenodo (2022)

Fawkes: Protecting Privacy against Unauthorized Deep Learning Models [article]

Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, Ben Y. Zhao
2020 arXiv   pre-print

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2020 pre-print
arXiv:2002.08327v1 fatcat:z3je6mmohrf2fanuf4vszzvd7e
Today's proliferation of powerful facial recognition systems poses a real threat to personal privacy.  ...  We need tools to protect ourselves from potential misuses of unauthorized facial recognition systems. Unfortunately, no practical or effective solutions exist.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agencies.  ... 
arXiv:2002.08327v2 fatcat:ceqrpfififehtcsakqw6cp6ecm
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Shawn Shan, Emily Wenger, Jiayun Zhang, Huiying Li, Haitao Zheng, Ben Y. Zhao. "Fawkes: Protecting Privacy against Unauthorized Deep Learning Models." arXiv (2020)

Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy

Helena Montenegro, Wilson Silva, Alex Gaudio, Matt Fredrikson, Asim Smailagic, Jaime S. Cardoso
2022 IEEE Access  

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Finally, we identify and propose new lines of research to guide future work in the generation of privacy-preserving case-based explanations.  ...  In this work, we identify the main limitations and challenges in the anonymization of case-based explanations of image data through a survey on case-based interpretability and image anonymization methods  ...  In the context of privacy-preserving methods, these models are applied to generate privatized images.  ... 
doi:10.1109/access.2022.3157589 fatcat:nuehxhxtw5a2rklceqv233foum
DOAJ
Citation

Helena Montenegro, Wilson Silva, Alex Gaudio, Matt Fredrikson, Asim Smailagic, Jaime S. Cardoso. "Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy." IEEE Access (2022) 28333-28347

PrivPAS: A real time Privacy-Preserving AI System and applied ethics [article]

Harichandana B S S, Vibhav Agarwal, Sourav Ghosh, Gopi Ramena, Sumit Kumar, Barath Raj Kandur Raja
2022 arXiv   pre-print

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2022 pre-print
arXiv:2202.02524v1 fatcat:san2yxruqvcdzni22dat66euqa
This motivates us to work towards a solution to generate privacy-conscious cues for raising awareness in smartphone users of any sensitivity in their viewfinder content.  ...  Such unauthorized image captures may also be misused to gain sympathy by third-party organizations, leading to a privacy breach.  ...  Unlike generic human subject awareness detection in images, this work focuses on using facial features recognition to determine the awareness of a select group of individuals as a proxy to their potential  ... 
arXiv:2202.02524v2 fatcat:fwg4jwyptnfl3ppamewa6oo4ba
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Harichandana B S S, Vibhav Agarwal, Sourav Ghosh, Gopi Ramena, Sumit Kumar, Barath Raj Kandur Raja. "PrivPAS: A real time Privacy-Preserving AI System and applied ethics." arXiv (2022)

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