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When Machine Learning Meets Privacy: A Survey and Outlook
[article]
2020
arXiv
pre-print
Preserved Fulltext
The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning aided privacy protection, and (iii) machine learning-based privacy ...
It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both ...
Object detection is used for identifying sensitive information. Additionally, schemes discussed in 4.2.1 do not directly provide privacy protection. ...
arXiv:2011.11819v1
fatcat:xuyustzlbngo3ivqkc4paaer5q
When Machine Learning Meets Privacy
2021
ACM Computing Surveys
Preserved Fulltext
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The survey covers three categories of interactions between privacy and machine learning: (i) private machine learning, (ii) machine learning-aided privacy protection, and (iii) machine learning-based privacy ...
It is important to note that the problem of privacy preservation in the context of machine learning is quite different from that in traditional data privacy protection, as machine learning can act as both ...
Object detection is used for identifying sensitive information. Additionally, schemes discussed in 4.2.1 do not directly provide privacy protection. ...
doi:10.1145/3436755
fatcat:cbkbmxj7krc3xoedv6tan4fle4
Deep Learning in Information Security
[article]
2018
arXiv
pre-print
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Machine learning techniques learn models from data representations to solve a task. These data representations are hand-crafted by domain experts. ...
Deep Learning is a sub-field of machine learning, which uses models that are composed of multiple layers. ...
IPrivacy: Image Privacy Protection
by Identifying Sensitive Objects via Deep Multi-Task Learning. IEEE Transactions on Information Forensics
and Security, 12(5):1005–1016, 2017. ...
arXiv:1809.04332v1
fatcat:xfb7lgrkw5cirdl3qvmg3ssnbi
PrivacEye: Privacy-Preserving Head-Mounted Eye Tracking Using Egocentric Scene Image and Eye Movement Features
[article]
2018
arXiv
pre-print
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To close the shutter in privacy-sensitive situations, the method uses a deep representation of the first-person video combined with rich features that encode users' eye movements. ...
We evaluate our method on a first-person video dataset recorded in daily life situations of 17 participants, annotated by themselves for privacy sensitivity, and show that our method is effective in preserving ...
social images users are willing to share using deep multi-task learning [58] . ...
arXiv:1801.04457v2
fatcat:sfrsmgirnvgivihlxg4bhdotx4
A Survey on Privacy for B5G/6G: New Privacy Goals, Challenges, and Research Directions
[article]
2022
arXiv
pre-print
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This paper provides a comprehensive survey on privacy-related aspects for B5G/6G networks. First, it discusses a taxonomy of different privacy perspectives. ...
Additionally, this paper discusses the emerging field of non-personal data privacy. It also provides an overview of standardization initiatives for privacy preservation. ...
ACKNOWLEDGEMENT This work is partly supported by European Union in SPA-TIAL (Grant No: 101021808), Academy of Finland in 6Genesis (grant no. 318927) and Science Foundation Ireland under CONNECT phase 2 ...
arXiv:2203.04264v1
fatcat:blbzfjy73fhlvammlwsgfmso5y
Adversarial Images Against Super-Resolution Convolutional Neural Networks for Free
2022
Proceedings on Privacy Enhancing Technologies
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In this work, we hypothesize and empirically show that adversarial examples learned over CNN image classifiers can survive processing by SRCNNs and lead them to generate poor quality images that are hard ...
We demonstrate that a user with a small CNN is able to learn adversarial noise without requiring any customization for SRCNNs and thwart the privacy threat posed by a pipeline of SRCNN and CNN classifiers ...
Fan. iprivacy: Image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans. Information Forensics and Security, 12(5):1005-1016, 2017. [63] R. ...
doi:10.56553/popets-2022-0065
fatcat:aew2plqqdvc4hlfgj37yc4vlsm
Intelligent privacy safeguards for the digital society
[article]
2023
Preserved Fulltext
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[edited by Author] ...
by "adversaries". ...
instance learning algorithm is adopted to identify 280 privacy sensitive object classes and events. ...
doi:10.14273/unisa-4580
fatcat:jfxwwivegvfdllnsdotc5ynndm
A DISTRIBUTED APPROACH TO PRIVACY ON THE CLOUD
[article]
2012
Preserved Fulltext
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The increasing adoption of Cloud-based data processing and storage poses a number of privacy issues. ...
Users wish to preserve full control over their sensitive data and cannot accept it to be fully accessible to an external storage provider. ...
Basically, sensitive attributes (singleton constraints) need to be encrypted, while sensitive associations can be protected by splitting (fragmenting) the involved attributes among the two servers. ...
doi:10.13130/pagano-francesco_phd2012-03-06
fatcat:jgruu2qupzgwnprcq7d4h5rjaa