![]() ![]() She actively works on the design, implementation, and evaluation of several strengths-based youth programmes in Hong Kong and mainland China, such as the Project P.A.T.H.S. Dr Yu led a few projects on the development and validation of assessment tools on mental health, developmental skills, and wellbeing indicators for Chinese population. Her programme of research, grounded in a positive youth development approach and a public health perspective, has been focusing on advancing knowledge about the identification of risk and protective factors, effective prevention and intervention strategies to promote wellbeing and to address problems of Chinese youth. Departmental Policy for Credit Transferĭr Lu Yu was trained in clinical medicine, psychiatry, education, and positive psychology.General University Requirements (GUR) Subjects. ![]() Minor in Social Policy and Administration (54439-YPA).Minor in Applied Psychology (54446-YAP).Minor in Social Policy and Social Entrepreneurship (54439-YPE).Honorary/Adjunct/Visiting Appointments and Professors of Practice.The graduate courses she has taught are mainly security-related, including computer network security (ECE4490/6490), malware reverse engineering (ECE8830), and distributed denial-of-service (DDoS) attacks (ECE8860). She has taught System Programming Concepts (ECE2220) and Communications systems (ECE4270). Yu has taught both undergraduate and graduate courses. In particular, her previous work includes privacy-preserving probabilistic counting, and negative surveys. True user privacy is only possible when no personally identifiable information (PII) is ever collected. Yu’s research focuses on privacy-preserving data collection approaches. Privacy-preserving data techniques look at techniques that useful statistics from user data without disclosing any private user information. Privacy-preserving data mining techniques are critical to protect user privacy in the era of big data. The application of the LWM algorithm includes clinical trials, transparency and accountability of agriculture supply chain, and digital forensics, etc. This allows consensus to be achieved with minimal computational overhead. Unlike most existing mining algorithms (e.g., Proof-of-Work (PoW), Proof-of-Stake (PoS)) that are devised for cryptocurrency applications, LWM is completely free of monetary qualities. The lightweight mining (LWM) consensus algorithm guarantees a c as long as there is one good miner. A reliable, efficient and secure consensus algorithm is critical to the blockchain technology. Her previous research includes botnet detection (e.g., domain name generation (DGA)), distributed denial-of-service (DDoS) attacks detection and mitigation, and Markov decision processes (MDP), etc.īlockchain is a distributed digital ledger storing provenance metadata. She is currently engaged in research in privacy-preserving data mining, blockchain technology, and anonymity network. Yu’s research interests mainly focus on cybersecurity. 2004, Xi’an Jiaotong University Information Engineering Contact Information Office: 300B Riggs Hall Office Phone: 864.656.5916 Email: Dr. 2007, Xi’an Jiaotong University Control Theory and Control Engineering B.S. 2012, Clemson University Electrical Engineering B.S. Research Assistant Professor of Electrical and Computer Engineering ![]()
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