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(1 - 4 of 4)
- Title
- POWER GRID VERIFICATION ON CLOUD
- Creator
- Gupte, Naval
- Date
- 2016, 2016-05
- Description
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Reliability and performance of modern ICs is becoming increasingly susceptible to supply voltage variations. Increased demand for low voltage...
Show moreReliability and performance of modern ICs is becoming increasingly susceptible to supply voltage variations. Increased demand for low voltage integrated circuits has made power grid analysis extremely critical and indispensable in modern design flows. Efficient validation of on-chip power distribution network is computationally demanding because of increasing grid sizes. Power grid simulation is critical for analysis and verification of power supply noises for robust and reliable IC designs. Computational demands to simulate power grids for ICs with increasing complexity is never-ending. Cloud computing platforms can be leveraged to mitigate costs associated with making these resources available. However, since simulation data usually contains sensitive design information, simulating on third-party platforms lead to major security concerns. In this study, we propose a framework for secure power grid simulation on Cloud. A transformation algorithm to hide current excitations is presented, while still allowing a majority of computations to be completed on Cloud. We employ multiple compression strategies to significantly reduce communication and storage overheads. Experiments show that our framework can achieve similar turn-around time as an insecure simulator on Cloud, while securing current excitations and output voltage vectors with reasonable communication and computational overheads. Vectorless technique to grid verification estimates worst-case voltage noises without detailed enumeration of load current excitations. We study voltage noise assessment in RLC models of VDD and GND networks in integrated power grids. Abstract grid model is utilized to abbreviate runtime, while transient constraints capture transitory circuit behaviour. Heuristics are employed to extract constraints that restrict power consumption profiles to realistic scenarios. Multiple linear programming problems are formulated to evaluate bounds on voltage overshoots and undershoots. We propose ways to mitigate storage and computational requirements on processing resources, enabling users to deploy computations on economical Cloud Computing platforms. Recommended solution is parallelizable, thereby reducing the overall verification time. Data compression is applied to fully exploit the compute capabilities of contemporary processors for higher throughputs. Experimental results suggest that the proposed technique is practical and scalable for industrial grids.
Ph.D. in Electrical Engineering, May 2016
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- Title
- CYBER PHYSICAL SYSTEM WITH COUPLED NETWORKS: SECURITY AND PRIVACY
- Creator
- Zhao, Jing
- Date
- 2019
- Description
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With the development of cyber physical systems, people and electronic devices are connected via various networks. In many scenarios, different...
Show moreWith the development of cyber physical systems, people and electronic devices are connected via various networks. In many scenarios, different networks are strongly coupled with each other, e.g. power grid is strongly coupled with the communication network in smart grid. On one hand, such coupling brings benefits such as improved efficiency and quick response to system service exceptions. However, the coupling of different networks also brings security and privacy problems. In this thesis we study two scenarios: the the secure coupling of visual connection with short range pairwise communication and privacy aware coupling of smart home with smart grid. For the first scenario, we propose SCsec, a secure screen-camera communication system, which achieves secure one-way communication. The throughput of SCsec is comparable to current screen communication systems. For the second scenario, we propose a novel randomized battery load hiding algorithm which ensures differential privacy for smart homes with smart meters.
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- Title
- ENHANCING PRIVACY AND SECURITY IN IOT-BASED SMART HOME
- Creator
- Du, Haohua
- Date
- 2019
- Description
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The IoT-based smart home is envisioned as a system that augments everyone’s daily life. In the past few years, the smart home attracted...
Show moreThe IoT-based smart home is envisioned as a system that augments everyone’s daily life. In the past few years, the smart home attracted immense attention from the industrial organizations and has been considered as one of the principal pillars of the fourth industrial revolution. However, while the rapidly increasing number of Internet-connected smart devices expends the functionalities of smart homes, it also raises substantial security and privacy concerns.Commonly, a smart home system is composed of three major components, smart devices, communication among devices, and smart applications connecting the devices. Thus, this dissertation aims to enhance the security and privacy of the smart home system without weakening its functionalities from the perspectives of these three components. First, I improve the security of smart devices within the smart home by monitoring their behaviors based on the contextual environment. Then, I enhance the security of the communications among the devices through visible light communication, whose receivers have to be physically visible to senders and avoid possible eavesdropping. Finally, I study two popular smart applications – the augmented reality assistant and the cloud-based surveillance system, to discuss how to define privacy, how to reduce the leakage, and how to balance the privacy and security in the smart home. This dissertation proposes the mechanisms for each component, respectively, and it also implements the design in the real-world for evaluating their effectiveness and efficiency.
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- Title
- Towards Trustworthy Multiagent and Machine Learning Systems
- Creator
- Xie, Shangyu
- Date
- 2022
- Description
-
This dissertation aims to systematically research the "trustworthy" Multiagent and Machine Learning systems in the context of the Internet of...
Show moreThis dissertation aims to systematically research the "trustworthy" Multiagent and Machine Learning systems in the context of the Internet of Things (IoT) system, which mainly consists of two aspects: data privacy and robustness. Specifically, data privacy concerns about the protection of the data in one given system, i.e., the data identified to be sensitive or private cannot be disclosed directly to others; robustness refers to the ability of the system to defend/mitigate the potential attacks/threats, i.e., maintaining the stable and normal operation of one system.Starting from the smart grid, a representative multiagent system in the IoT, I demonstrate two works on improving data privacy and robustness in aspects of different applications, load balancing and energy trading, which integrates secure multiparty computation (SMC) protocols for normal computation to ensure data privacy. More significantly, the schemes can be readily extended to other applications in IoT, e.g., connected vehicles, mobile sensing systems.For the machine learning, I have studied two main areas, i.e., computer vision and natural language processing with the privacy and robustness correspondingly. I first present the comprehensive robustness evaluation study of the DNN-based video recognition systems with two novel proposed attacks in both test and training phase, i.e., adversarial and poisoning attacks. Besides, I also propose the adaptive defenses to fully evaluate such two attacks, which can thus further advance the robustness of system. I also propose the privacy evaluation for the language systems and show the practice to reveal and address the privacy risks in the language models. Finally, I demonstrate a private and efficient data computation framework with the cloud computing technology to provide more robust and private IoT systems.
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