Confidential Information

This study intends to investigate the ability of Elliott Waves Theory (EWT) to predict the information content of accounting profit before its announcement in companies listed on the Tehran Stock Exchange from 1394 to 1399(Iranian calendar). The present study is applied research in terms of the result’s implementation and to collect the data required for the research, the information site of the publishers of Tehran Stock Exchange and Rahavard Novin software and for receiving Elliot Waves signals Advanced GET software has been used. Earnings information content was measured through the test of the relationship between earnings and abnormal returns based on the Portetti model [20]. In order to calculate the abnormal returns, the Zebrowski comprehensive return relationship was used. Finally, the effect of two variables, company size and type of industry, on the ability to predict information content is considered. The results showed that the quarterly profits announced by companies have information content, and EWT can predict profit information content before announcing it. In this regard, company size and industry type do not affect Elliott Waves’ ability to predict profit information content.

The widespread adoption of Android phones has heightened concerns about user privacy. This research presents an Adaptive Privacy Management System (APMS) that integrates Machine Learning (ML) models with Differential Privacy techniques to enhance privacy protection. The APMS monitors application behavior and employs ML algorithms to detect anomalies and enable context-aware privacy enforcement. Differential Privacy ensures that sensitive data remains protected through the addition of noise and privacy-preserving computations. Experimental results demonstrate that the APMS achieves a 92.5% accuracy rate in detecting the privacy leakage. The anomaly detection model, using Random Forest, shows high accuracy (92.5%), recall (89.5%), and precision (73.9%), effectively identifying both normal and anomalous behaviors. Additionally, the impact of noise on data utility, controlled by the privacy budget (ε), is manageable. The results show that APMS is a robust system for safeguarding user confidential information, contributing to a more secure and privacy-centric Android ecosystem.