User privacy leak detection method, server and system based on multi-task learning
A multi-task learning and user privacy technology, applied in the field of information classification processing, can solve the problems of poor model interpretability and neglect of privacy correlation, so as to reduce the risk of privacy leakage and improve the modeling performance and efficiency.
Active Publication Date: 2021-11-30
SHANDONG UNIV
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Existing techniques are mainly applied to structured data. For unstructured data, effective classifiers are usually trained intensively, but they mainly focus on coarse-grained privacy judgments, ignoring the correlation of privacy, making the model less interpretable
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[0043] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
[0044] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.
[0045] figure 1 It is a flowchart of a user privacy leakage detection method based on multi-task learning in th...
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The invention discloses a user privacy leakage detection method, server and system based on multi-task learning. Among them, the user privacy leakage detection method based on multi-task learning includes: in order to fully characterize user privacy, user privacy is divided into several fine-grained privacy categories in advance, and these fine-grained privacy categories are divided into several groups to form user privacy groups Structural information; extract user privacy features from different directions to fully characterize user privacy categories; build a prediction model based on the extracted user privacy features; and introduce multi-task learning, each task in the same group shares related features The cable model takes the group structure information of user privacy as a priori and performs feature grouping, thereby improving the modeling performance of user privacy leak detection and the interpretability of the model.
Description
technical field [0001] The invention belongs to the field of information classification processing, and in particular relates to a user privacy leakage detection method, server and system based on multi-task learning. Background technique [0002] With the rapid development of Web2.0, the Internet has entered the era of social media, and social media has become an important platform for people to obtain and share information on a daily basis. As users are increasingly exposed to social media, privacy threats have gradually become an important issue related to the vital interests of every online user. [0003] In recent years, there have been many research works around the detection of user privacy leaks in social media. Existing techniques are mainly applied to structured data. For unstructured data, effective classifiers are usually trained intensively, but they mainly focus on coarse-grained privacy judgments and ignore the correlation of privacy, making the model less in...
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IPC IPC(8): G06F21/62
CPCG06F21/6245
Inventor 宋雪萌陈潇琳程志勇王英龙聂礼强
Owner SHANDONG UNIV