Image privacy decision recommendation system and method under social network on the basis of privacy rule and perceptual Hash

Abstract

The invention discloses an image privacy decision recommendation system and method under a social network on the basis of a privacy rule and perceptual Hash. The invention is characterized in that the system comprises a privacy object registration module, a privacy rule definition module and a privacy decision recommendation module, wherein the privacy object registration module provides a registration function of an object to be protected for a user; the privacy rule definition module defines a privacy security level for a to-be-protected objet of the user; and the privacy decision recommendation module identifies a to-be-uploaded object of the user, compares the to-be-uploaded object of the user with a protected object in a Hash fingerprint database and a privacy rule in a privacy rule base, and finally submits the privacy security level of the to-be-uploaded object to the privacy decision recommendation module of the user. The quick and convenient automatic calculation of the privacy level and decision recommendation can be realized, and the leakage risks of individuals and the families of the individual can be effectively lowered so as to greatly improve the protection safety of user individual image privacy.

Description

technical field [0001] The invention belongs to the field of privacy protection of social network images, in particular to a method for making privacy decision-making recommendations for social network images in combination with semantic rules. Background technique [0002] The rapid development of the mobile Internet has made it very common for people to log in to social networks on mobile terminals. At the same time, the use of digital images in the Internet is becoming more and more extensive. Text-based social networking has evolved into image-based social networking, and the rich amount of information brought by images more satisfies people's needs for information sharing and obtaining. However, because images carry richer information, it is easier to leak user privacy. For example, in daily life, a picture randomly uploaded by a social network user is likely to reveal an item he owns or his relationship with another person in the photo, and this information can provi...

AI Technical Summary

Problems solved by technology

[0005] 2. The current image privacy protection method is manually selected by the user. The process is cumbersome and error-prone. The system cannot provide intelligent privacy decisions when users upload images, and cannot help users with low self-protection awareness such as teenagers or when they are drunk Delicious users provide privacy decision support

[0006] 3. The existing image privacy protection technology does not attach semantic information to the content to be protected

[0007] 4. Some large social network companies abroad, such as Facebook, try to use machine learning (Deep Learning, Deep Learning) to solve social network privacy protection and decision-making recommendation problems, but machine learning-based methods require a large number of sample learning in advance. There are deficiencies in efficiency, personalization, etc. At present, deep learning technology has not been developed enough, and it cannot meet the real-time needs of social network digital image privacy decision-making.

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