Decision Tree Classification Scheme Research Articles

Secure decision tree classification with decentralized authorization and access control

Outsourcing decision tree classification services to the cloud is highly beneficial, yet raises critical privacy problems. In order to preserve data privacy, data owners may choose to upload encrypted data rather than raw data to the classification services. However, these solutions adopted today for encrypted data classification not only fall short in system flexibility and scalability, but also face the single point of failure problem. In this paper, we design, implement, and evaluate a secure decision tree classification scheme that allows decentralized authorization and access control service (SDTC-DAAC). Firstly, we propose a new framework that decouples data encryption and data computation logic to achieve the separation of data storage and computation, which significantly improves upon the flexibility and effectiveness, thus achieving cross-system compatibility requirements. Secondly, we present an end-to-end encrypted access control mechanism which enables authorized users from different parties to participate in calculations together. Finally, we further devise a scheme which serves decentralized storage service of data access control policies and access authorization without trusted intermediaries. Extensive property and performance analysis shows that SDTC-DAAC is effectiveness, as well as satisfying the security requirements for data privacy in an outsourcing environment.

Efficient and Secure Decision Tree Classification for Cloud-Assisted Online Diagnosis Services

Decision tree classification has become a prevailing technique for online diagnosis services. By outsourcing computation intensive tasks to a cloud server, cloud-assisted online diagnosis services are better ways for cases that the storage and computation requirements exceed the capability of medical institutions. With privacy concerns as well as intellectual property protection issues, the valuable diagnosis classifier and the sensitive user data should be protected against the cloud server. In this paper, we identify a work-flow for cloud-assisted online diagnosis services. We propose an efficient and secure decision tree classification scheme in the proposed work-flow. Specifically, the medical institution transforms a locally pre-trained decision tree classifier to a decision table, and later uses searchable symmetric encryption to encrypt the decision table. Then, the encrypted table is outsourced to the cloud server, and a user can submit encrypted physiological features to the cloud server and obtain an encrypted diagnosis prediction back. We provide formal security proofs to demonstrate that our scheme protects the confidentiality of the decision tree classifier and the user's data. The performance analysis shows that our scheme achieves faster-than-linear classification speed. Experimental evaluations show that our scheme requires several micro-seconds to process a diagnosis request in the tested datasets.

Land cover in East Asia classified using Terra MODIS and DMSP OLS products

Land cover is classified over East Asia using 250‐m Moderate Resolution Imaging Spectroradiometer (MODIS) land surface reflectance, MODIS snow cover and Operational Linescan System (OLS) human settlement data. The classification method includes a decision tree classification scheme that considers 11 kinds of land surface features derived from the OLS product and the time series of two MODIS products in 2000. The decision tree was defined manually based on the experiment because of insufficient training data, ease of tuning by visual interpretation, and extensibility to further research. The resulting classification is compared to three kinds of reference data, i.e. MODIS land cover product, Chinese digital land cover map, and Chinese census. The land cover classification can be input into a hydrological model applied to the Yellow River in China.

AUTOMATED DIAGNOSIS OF BRAIN TUMOURS ASTROCYTOMAS USING PROBABILISTIC NEURAL NETWORK CLUSTERING AND SUPPORT VECTOR MACHINES

A computer-aided diagnosis system was developed for assisting brain astrocytomas malignancy grading. Microscopy images from 140 astrocytic biopsies were digitized and cell nuclei were automatically segmented using a Probabilistic Neural Network pixel-based clustering algorithm. A decision tree classification scheme was constructed to discriminate low, intermediate and high-grade tumours by analyzing nuclear features extracted from segmented nuclei with a Support Vector Machine classifier. Nuclei were segmented with an average accuracy of 86.5%. Low, intermediate, and high-grade tumours were identified with 95%, 88.3%, and 91% accuracies respectively. The proposed algorithm could be used as a second opinion tool for the histopathologists.