Privacy Compliance Detection Methods for Applications
Facing a severe regulatory landscape on personal information protection,a method for scientifically assessing privacy compliance issues in applications(APPs)is proposed.This method utilized a BERT-TextCNN classification model for privacy compliance detection.Taking into full consideration of the existing domestic laws and regulations,a comprehensive set of priva-cy compliance evaluation indicators was established.Through manual annotation,a specialized corpus for privacy compliance was built.The BERT-TextCNN classification model was then trained on this corpus to accurately extract key points from the privacy policies of APPs.Ultimately,the model's predictions were used to calculate a privacy compliance score,providing a thorough examination for potential privacy compliance risks in the APPs.This approach ensures the integrity of the privacy pol-icies.Experimental results demonstrated the outstanding performance of the model in efficiently and accurately extracting priva-cy compliance points and detecting compliance risks in APPs.This innovative method holds the promise of guiding enterprises and developers in avoiding the risks associated with regulatory notifications.Simultaneously,it provides valuable references for regulatory authorities,contributing positively to safeguarding individuals'privacy rights.
application programprivacy protection technologytext classificationdeep learningindex system
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