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Distributional drift detection is important in medical applications as it helps ensure the accuracy and reliability of models by identifying changes in the underlying data distribution that could affect the prediction results of machine learning models. However, current methods have limitations in detecting drift, for example, the inclusion of abnormal datasets can lead to unfair comparisons. This paper presents an accurate and sensitive approach to detect distributional drift in CT-scan medical images by leveraging data-sketching and fine-tuning techniques. We developed a robust baseline library model for real-time anomaly detection, allowing for efficient comparison of incoming images and identification of anomalies. Additionally, we fine-tuned a pre-trained Vision Transformer model to extract relevant features, using mammography as a case study, significantly enhancing model accuracy to 99.11%. Combining with data-sketches and fine-tuning, our feature extraction evaluation demonstrated that cosine similarity scores between similar datasets provide greater improvements, from around 50% increased to 99.1%. Finally, the sensitivity evaluation shows that our solutions are highly sensitive to even 1% salt-and-pepper and speckle noise, and it is not sensitive to lighting noise (e.g., lighting conditions have no impact on data drift). The proposed methods offer a scalable and reliable solution for maintaining the accuracy of diagnostic models in dynamic clinical environments. 

While our society accelerates its transition to the Internet of Things, billions of IoT devices are now linked to the network. While these gadgets provide enormous convenience, they generate a large amount of data that has already beyond the network’s capacity. To make matters worse, the data acquired by sensors on such IoT devices also include sensitive user data that must be appropriately treated. At the moment, the answer is to provide hub services for data storage in data centers. However, when data is housed in a centralized data center, data owners lose control of the data, since data centers are centralized solutions that rely on data owners’ faith in the service provider. In addition, edge computing enables edge devices to collect, analyze, and act closer to the data source, the challenge of data privacy near the edge is also a tough nut to crack. A large number of user information leakage both for IoT hub and edge made the system untrusted all along. Accordingly, building a decentralized IoT system near the edge and bringing real trust to the edge is indispensable and significant. To eliminate the need for a centralized data hub, we present a prototype of a unique, secure, and decentralized IoT framework called Reja, which is built on a permissioned Blockchain and an intrusion-tolerant messaging system ChiosEdge, and the critical components of ChiosEdge are reliable broadcast and BFT consensus. We evaluated the latency and throughput of Reja and its sub-module ChiosEdge. 

We present Chios, an intrusion-tolerant publish/subscribe system which protects against Byzantine failures. Chios is the first publish/subscribe system achieving decentralized confidentiality with fine-grained access control and strong publication order guarantees. This is in contrast to existing publish/subscribe systems achieving much weaker security and reliability properties. Chios is flexible and modular, consisting of four fully-fledged publish/subscribe configurations (each designed to meet different goals). We have deployed and evaluated our system on Amazon EC2. We compare Chios with various publish/subscribe systems. Chios is as efficient as an unreplicated, single-broker publish/subscribe implementation, only marginally slower than Kafka and Kafka with passive replication, and at least an order of magnitude faster than all Hyperledger Fabric modules and publish/subscribe systems using Fabric.