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. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharingimages on OSNs . However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing  . Specifically, we present a  high-level  analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field . The framework is divided into three main stages : local management, online management and social experience . At each stage, we identify typical sharing-related user behaviors , the privacy issues generated by those behaviors, and review representative intelligent solutions  . The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets .
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. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs in OSN image sharing . However, OSN image privacy itself is quite complicated, and solutions currently in place for privacy management in reality are insufficient to provide personalized, accurate and flexible privacy protection. A more intelligent environment for privacy-friendly OSN image sharing is in demand. 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 . Specifically, we present a definition and a taxonomy of OSN image privacy, and a high-level privacy analysis framework based on the lifecycle of OSN image sharing . The framework consists of three stages with different principles of privacy by design . At each stage, we identify typical user behaviors in OSN image sharing and the privacy issues associated with these behaviors. Then a systematic review on the representative intelligent solutions targeting those privacy issues is conducted, also in a stage-based manner . The resulting analysis describes an intelligent privacy firewall for closed-loop privacy management. We also discuss the challenges and future directions in this area .
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when sharingimages on OSNs
in OSN image sharing
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sharing itself is relatively
privacy itself is quite
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systems
solutions
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to manage privacy in practice are labor-intensive yet fail
for privacy management in reality are insufficient
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As a result, an
A
R
systematic survey of 'privacy intelligence' solutions that target
survey of "privacy intelligence" that targets
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related to
in dynamic
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from a user-centric perspective
A
definition and a taxonomy of OSN image privacy, and a
A
privacy
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entire
D
to address the various privacy issues and solutions facing this interdisciplinary field
R
is divided into three main stages : local management, online management and social experience
consists of three stages with different principles of privacy by design
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sharing-related user behaviors ,
user behaviors in OSN image sharing and
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generated by those behaviors, and review
associated with these behaviors. Then a systematic review on the
A
targeting those privacy issues is conducted, also in a stage-based manner
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privacy-enhancing chain
privacy firewall
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existing at each stage, as well as in publicly available datasets
in this area