Stock Market Simulation

In this Interactive Qualifying Project (IQP), a five-week stock simulation was conducted using the technical trading method. Trades were made based on technical trading strategy and real-time news. Each week, investment results were analyzed and compare with three indices: S&P 500, Dow 30 and Nasdaq. Doing this IQP helped me to have a solid understanding of the stock market and experience different situations to become more competent and confident in the future

Privacy-preserving machine learning system at the edge

Data privacy in machine learning has become an urgent problem to be solved, along with machine learning's rapid development and the large attack surface being explored. Pre-trained deep neural networks are increasingly deployed in smartphones and other edge devices for a variety of applications, leading to potential disclosures of private information. In collaborative learning, participants keep private data locally and communicate deep neural networks updated on their local data, but still, the private information encoded in the networks' gradients can be explored by adversaries. This dissertation aims to perform dedicated investigations on privacy leakage from neural networks and to propose privacy-preserving machine learning systems for edge devices. Firstly, the systematization of knowledge is conducted to identify the key challenges and existing/adaptable solutions. Then a framework is proposed to measure the amount of sensitive information memorized in each layer's weights of a neural network based on the generalization error. Results show that, when considered individually, the last layers encode a larger amount of information from the training data compared to the first layers. To protect such sensitive information in weights, DarkneTZ is proposed as a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against neural networks. The performance of DarkneTZ is evaluated, including CPU execution time, memory usage, and accurate power consumption, using two small and six large image classification models. Due to the limited memory of the edge device's TEE, model layers are partitioned into more sensitive layers (to be executed inside the device TEE), and a set of layers to be executed in the untrusted part of the operating system. Results show that even if a single layer is hidden, one can provide reliable model privacy and defend against state of art membership inference attacks, with only a 3% performance overhead. This thesis further strengthens investigations from neural network weights (in on-device machine learning deployment) to gradients (in collaborative learning). An information-theoretical framework is proposed, by adapting usable information theory and considering the attack outcome as a probability measure, to quantify private information leakage from network gradients. The private original information and latent information are localized in a layer-wise manner. After that, this work performs sensitivity analysis over the gradients \wrt~private information to further explore the underlying cause of information leakage. Numerical evaluations are conducted on six benchmark datasets and four well-known networks and further measure the impact of training hyper-parameters and defense mechanisms. Last but not least, to limit the privacy leakages in gradients, I propose and implement a Privacy-preserving Federated Learning (PPFL) framework for mobile systems. TEEs are utilized on clients for local training, and on servers for secure aggregation, so that model/gradient updates are hidden from adversaries. This work leverages greedy layer-wise training to train each model's layer inside the trusted area until its convergence. The performance evaluation of the implementation shows that PPFL significantly improves privacy by defending against data reconstruction, property inference, and membership inference attacks while incurring small communication overhead and client-side system overheads. This thesis offers a better understanding of the sources of private information in machine learning and provides frameworks to fully guarantee privacy and achieve comparable ML model utility and system overhead with regular machine learning framework.Open Acces

Competition and subsidies in the deregulated US local telephone industry

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/115904/1/rand12109.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/115904/2/rand12109-sup-0001-SupMat.pd

Designing Repeatable Self-Healing into Cementitious Materials

Designing self-healing into cementitious materials can open a new world of opportunities for resilient concrete infrastructure under service loading conditions. The self-healing process should be robust as well as repeatable, allowing for self-repair after multiple damage events. The repeatability poses great challenges when self-healing strategies mainly rely on the formation of low-strength calcium carbonate healing product, complicated by the localized cracking behavior of cementitious materials. This study aims at formulating a new cementitious material system with designed physical and chemical characteristics that favour repeatable self-healing. Advanced experimental methods, coupled with micromechanics theory, are adopted to probe and design repeatable self-healing into cementitious materials. This study aims at formulating a new cementitious material system with designed physical and chemical characteristics that favor repeatable self-healing. This is achieved by answering fundamental questions such as what is the dominating self-healing mechanism within a crack, how do self-healing products grow, and what are the physical and chemical variables that influence the self-healing mechanism under certain environmental exposure conditions. Advanced experimental methods, coupled with micromechanics theory, are adopted to probe and design repeatable self-healing into cementitious materials

Stability of Phase-modulated Quantum Key Distribution System

Phase drift and random fluctuation of interference visibility in double unbalanced M-Z QKD system are observed and distinguished. It has been found that the interference visibilities are influenced deeply by the disturbance of transmission fiber. Theory analysis shows that the fluctuation is derived from the envioronmental disturbance on polarization characteristic of fiber, especially including transmission fiber. Finally, stability conditions of one-way anti-disturbed M-Z QKD system are given out, which provides a theoretical guide in pragmatic anti-disturbed QKD.Comment: 9 pages, 3 figue