Yonglong Luo

Privacy Preserving ID3 Algorithm over Horizontally Partitioned Data

Privacy preserving decision tree classification algorithm is to solve such a distributed computation problem that the participant parties jointly build a decision tree over the data set distributed among them, and they do not want their private sensitive data to be revealed to others during the tree-building process. The existing privacy preserving decision tree classification algorithms over the data set horizontally partitioned and distributed among different parties only can cope with the data with discrete attribute values. This paper propose a solution to privacy preserving C4.5 algorithm based on secure multi-party computation techniques, which can securely build a decision tree over the horizontally partitioned data with both discrete and continuous attribute values. Moreover, we propose a secure two-party bubble sort algorithm to solve the privacy preserving sort problem in our solution

Secure two-party point-circle inclusion problem

Privacy-preserving computational geometry is a special secure multi-party computation and has many applications. Previous protocols for determining whether a point is inside a circle are not secure enough. We present a two-round protocol for computing the distance between two private points and develop a more efficient protocol for the point-circle inclusion problem based on the distance protocol. In comparison with previous solutions, our protocol not only is more secure but also reduces the number of communication rounds and the number of modular multiplications significantly.

Detecting anomalies from big network traffic data using an adaptive detection approach

The unprecedented explosion of real-life big data sets have sparked a lot of research interests in data mining in recent years. Many of these big data sets are generated in network environment and are characterized by a dauntingly large size and high dimensionality which pose great challenges for detecting useful knowledge and patterns, such as network traffic anomalies, from them. In this paper, we study the problem of anomaly detection in big network connection data sets and propose an outlier detection technique, called Adaptive Stream Projected Outlier deTector (A-SPOT), to detect anomalies from large data sets using a novel adaptive subspace analysis approach. A case study of A-SPOT is conducted in this paper by deploying it to the 1999 KDD CUP anomaly detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well to better tailor A-SPOT to deal with the case study. Experimental results demonstrate that A-SPOT is effective and efficient in detecting anomalies from network data sets and outperforms existing detection methods.

Fingerprint ridge orientation field reconstruction using the best quadratic approximation by orthogonal polynomials in two discrete variables

This paper proposes a novel algorithm for reconstructing the fingerprint orientation field (FOF). The basic idea of the algorithm is to reconstruct the ridge orientation by using the best quadratic approximation by orthogonal polynomials in two discrete variables. We first estimate the local region orientation by the linear projection analysis (LPA) based on the vector set of point gradients, and then reconstruct the ridge orientation field using the best quadratic approximation by orthogonal polynomials in two discrete variables in the sine domain. In this way, we solve the problem that is difficult to accurately extract low quality fingerprint image orientation fields. The experiments with the database of FVC 2004 show that, compared to the state-of-the-art fingerprint orientation estimation algorithms, the proposed method is more accurate and more robust against noise, and is able to better estimate the FOF of low quality fingerprint images with large areas of noise.

On Efficient and Robust Anonymization for Privacy Protection on Massive Streaming Categorical Information

Protecting users’ privacy when transmitting a large amount of data over the Internet is becoming increasingly important nowadays. In this paper, we focus on the streaming categorical information and propose a novel anonymization technique for providing a strong privacy protection to safeguard against privacy disclosure and information tampering. Our technique utilizes an innovative two-phase anonymization approach which is very easy to implement, highly efficient in terms of speed and communication and is robust against possible tampering from adversaries. Extensive experimental evaluation that is conducted demonstrates that our technique is very efficient and more robust than the existing method.

Probability method for Cerenkov luminescence tomography based on conformance error minimization

Cerenkov luminescence tomography (CLT) was developed to reconstruct a three-dimensional (3D) distribution of radioactive probes inside a living animal. Reconstruction methods are generally performed within a unique framework by searching for the optimum solution. However, the ill-posed aspect of the inverse problem usually results in the reconstruction being non-robust. In addition, the reconstructed result may not match reality since the difference between the highest and lowest uptakes of the resulting radiotracers may be considerably large, therefore the biological significance is lost. In this paper, based on the minimization of a conformance error, a probability method is proposed that consists of qualitative and quantitative modules. The proposed method first pinpoints the organ that contains the light source. Next, we developed a 0-1 linear optimization subject to a space constraint to model the CLT inverse problem, which was transformed into a forward problem by employing a region growing method to solve the optimization. After running through all of the elements used to grow the sources, a source sequence was obtained. Finally, the probability of each discrete node being the light source inside the organ was reconstructed. One numerical study and two in vivo experiments were conducted to verify the performance of the proposed algorithm, and comparisons were carried out using the hp-finite element method (hp-FEM). The results suggested that our proposed probability method was more robust and reasonable than hp-FEM.

An intelligent recommender system based on predictive analysis in telehealthcare environment

The use of intelligent technologies for providing useful recommendations to patients suffering chronic diseases may play a positive role in improving the general life quality of patients and help reduce the workload and cost involved in their daily healthcare. The objective of this study is to develop an intelligent recommender system based on predictive analysis for advising patients in the telehealth environment concerning whether they need to take the body test one day in advance by analyzing medical measurements of a patient for the past k days. The proposed algorithms supporting the recommender system have been validated using a time series telehealth data recorded from heart disease patients which were collected from May to January 2012, from our industry collaborator Tunstall. The experimental results show that the proposed system yields satisfactory recommendation accuracy and offer a promising way for saving the workload for patients to conduct body tests every day. This study highlights the possible usefulness of the computerized analysis of time series telehealth data in providing appropriate recommendations to patients suffering chronic diseases such as heart diseases patients.

Secure Two-Party Multi-Dimensional Vector Comparison Protocol

Secure two-party vector dominance means that the two sides of the computation want to judge whether their vectors have the dominance relation securely, a new problem which is called Multi-Dimensional Vector Comparison Problem is proposed based on the vector dominance in this paper, it can be applied in many commercial areas, such as auction, bidding and so on. Two solutions are provided for this problem, using multiplication protocol and scalar products protocol respectively. Furthermore, we introduce the random algorithm to solve the problem, analyze the security, correctness and complexity, point out the differences between these two methods, and simulate by experiments.

Coupling a Fast Fourier Transformation With a Machine Learning Ensemble Model to Support Recommendations for Heart Disease Patients in a Telehealth Environment

Recently, the use of intelligent technologies in clinical decision making in the telehealth environment has begun to play a vital role in improving the quality of patients’ lives and helping reduce the costs and workload involved in their daily healthcare. In this paper, an effective medical recommendation system that uses a fast Fourier transformation-coupled machine learning ensemble model is proposed for short-term disease risk prediction to provide chronic heart disease patients with appropriate recommendations about the need to take a medical test or not on the coming day based on analysing their medical data. The input sequence of sliding windows based on the patient’s time series data are decomposed by using the fast Fourier transformation in order to extract the frequency information. A bagging-based ensemble model is utilized to predict the patient’s condition one day in advance for producing the final recommendation. A combination of three classifiers–artificial neural network, least squares-support vector machine, and naive bayes–are used to construct an ensemble framework. A real-life time series telehealth data collected from chronic heart disease patients are utilized for experimental evaluation. The experimental results show that the proposed system yields a very good recommendation accuracy and offers an effective way to reduce the risk of incorrect recommendations as well as reduce the workload for heart disease patients in conducting body tests every day. The results conclusively ascertain that the proposed system is a promising tool for analyzing time series medical data and providing accurate and reliable recommendations to patients suffering from chronic heart diseases.

Privacy-Preserving Protocols for String Matching

String matching is a basic problem of string operation, and privacy-preserving string matching, as a special case of secure multi-party computation, has broad applications in auction, bidding and some other commercial areas. In this paper, some protocols are proposed to solve this private matching problem, the security and correctness are analyzed respectively, and the actual efficiency is tested by experiment. A protocol is also designed based on the BMH algorithm which is more efficient and conceals more private information.