• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.1-12
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• 2022

Besides unexpected growth perceived by IoT's, the variety and volume of threats have increased tremendously, making it a necessity to introduce intrusion detections systems for prevention and detection of such threats. But Intrusion Detection and Prevention System (IDPS) inside the IoT network yet introduces some unique challenges due to their unique characteristics, such as privacy inference, performance, and detection rate and their frequency in the dynamic networks. Our research is focused on the privacy inferences of existing intrusion prevention and detection system approaches. We also tackle the problem of providing unified a solution to implement the open-source IDPS in the IoT architecture for assessing the performance of IDS by calculating; usage consumption and detection rate. The proposed scheme is considered to help implement the human health monitoring system in IoT networks

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.137-142
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• 2021

Machine-learning systems have proven their worth in various industries, including healthcare and banking, by assisting in the extraction of valuable inferences. Information in these crucial sectors is traditionally stored in databases distributed across multiple environments, making accessing and extracting data from them a tough job. To this issue, we must add that these data sources contain sensitive information, implying that the data cannot be shared outside of the head. Using cryptographic techniques, Privacy-Preserving Machine Learning (PPML) helps solve this challenge, enabling information discovery while maintaining data privacy. In this paper, we talk about how to keep your data mining private. Because Data mining has a wide variety of uses, including business intelligence, medical diagnostic systems, image processing, web search, and scientific discoveries, and we discuss privacy-preserving in deep learning because deep learning (DL) exhibits exceptional exactitude in picture detection, Speech recognition, and natural language processing recognition as when compared to other fields of machine learning so that it detects the existence of any error that may occur to the data or access to systems and add data by unauthorized persons.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.199-211
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• 2009

Official statistics which are collected for governments and the community can be used to assess the effectiveness of governments' policies and programs. Thus, official statistics should be collected and presented based on correct findings. Erroneous official statistics will lead to lower quality results in assessing those policies and programs. Many statistical agencies, today, use on-line analytical processing (OLAP) data cubes which support OLAP tasks like aggregation and subtotals as a key part of their dissemination strategy of official statistics. Confidentiality protection in data cubes also should be made. However, sensitive parts of data cubes including micro data may be disclosed by malicious inferences. The authors have suggested an inference control process in OLAP data cubes which preventing erroneous cube creating and securing cubes against privacy breaches. The objective of this study is to establish a strategy for inference control of official statistics using the inference control process by taking the case of the Patent Application Expense Support Project.