Machine Learning Post Modification for Sensitive Data Exposure
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Solution Overview
Problem
Users inadvertently share sensitive information on social media, which can be exploited, leading to negative consequences, and existing methods for data protection are either ineffective or overly restrictive, such as digital abstinence, which limits access to services and experiences.
Innovation Solution
A system that uses machine learning classifiers to analyze post data, associate it with predefined categories based on correlation, generate sensitive data indicators, and recommend modifications to reduce the risk of exposing sensitive information, including altering post content or audience to lower the risk of data exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users share information on social media, then communication and interaction are facilitated, but sensitive information may be inadvertently disclosed
Solution Approach 1:
The system performs preliminary analysis of post content before publication using machine learning classifiers to identify sensitive information. By detecting potentially sensitive data upfront and providing modification recommendations, the system prevents harmful exposure while allowing users to maintain their sharing behavior on social media platforms.
2Object-affected harmful factors
If existing data protection methods are implemented, then sensitive information is protected, but access to services and experiences is limited
Solution Approach 1:
The system applies selective protection by identifying and flagging only specific portions of post content that contain sensitive information, rather than blocking entire posts or limiting user access to social media services. This localized approach allows users to maintain full access to services while protecting only the sensitive elements within their content.
Solution Approach 2:
The system provides feedback to users through modification recommendations that show them how to adjust their post content to reduce sensitive information exposure. This feedback loop enables users to understand what makes their content sensitive and make informed decisions about what to share, maintaining service accessibility while improving protection.
3Measurement precision
If machine learning analysis is performed on post data, then sensitive content is identified, but processing time and system complexity increase
Solution Approach 1:
The system segments the analysis process into distinct components: categorizing post data based on entities, analyzing content for sensitive information using specialized machine learning classifiers, and generating separate modification recommendations. This segmentation allows each component to be optimized independently, improving detection accuracy while managing system complexity through modular design.
Data Source
AI summary
An embodiment associates a user's post data that with a category from a predefined list of categories based on the post content. The embodiment analyzes, using machine learning, the post data for potentially sensitive content and generates a first sensitive data indicator identifying potentially sensitive information in the post data and an associated first confidence value. The embodiment generates explanatory data identifying a feature that contributed to the post data being identified as potentially sensitive, and generates a modified version of the post data that modifies the feature. The embodiment analyzes the modified post data for potentially sensitive content and generates a second sensitive data indicator and a second confidence value indicating that the post data is more likely to contain sensitive data than the modified post data. The embodiment alerts the user regarding the potentially sensitive data and recommends changing the post based on the modified feature value.