Developing Advanced Privacy Protection Mechanisms for Connected Automotive User Experiences

The transportation industry is experiencing an unprecedented revolution. This revolution is being led by the rapid development of connected and automated vehicle (CAV) technologies together with cloud-based mobility services featured with huge amount of data being generated, collected,and utilized. This big data trend provides not only business opportunities but also challenges. One of the challenges is data privacy which is inherently unavoidable due to the information sharing nature of such mobility services and the advancement in data analytics. In this thesis, privacy issues and corresponding countermeasure that related to connected vehicle landscape are comprehensively studied. First of all, an overview of the landscape of emerging mobility services is provided and several typical connected vehicle services are introduced. Then we analyze and characterize data that can be collected and shared in these services and point out potential privacy risks. In order to protect user privacy while ensuring service functionality, we develop novel privacy protection mechanisms for connected automotive user experiences. Specifically, we consider the whole life cycle of data collection and sharing. To support privacy preserving data collection, we design fine-grained and privacy-aware data uploading policies that ensure the balance between enforcing privacy requirements and keeping data utility, and implement a prototype that collects data from vehicle, smartphone, and smartwatch securely. To support privacy preserving data sharing, we demonstrate two kinds of risks, additional individual information inference and user de-anonymization, during data sharing through concrete attack designs. We also propose corresponding countermeasures to defend against such attacks and minimize user privacy risks. The feasibility of such attacks and our defense strategies are evaluated with real world vehicular data.Master of ScienceComputer and Information Science, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/143518/1/thesis_Huaxin_Apr24_FontEmbed.pdfDescription of thesis_Huaxin_Apr24_FontEmbed.pdf : Thesi

Structural and functional properties of OSA-starches made with wide-ranging hydrolysis approaches

Octenyl succinic anhydride modified starches (OSA-starches) are widely used as emulsifiers and stabilizers in the food industry. This study investigates the relationships between molecular structure and emulsifying and antioxidant properties of OSA-starches with a wide range of structures, formed by hydrolysis by α-amylase, β-amylase and HCl for various hydrolysis times. Structural parameters, namely molecular size distribution, chain-length distribution, degree of branching (DB) and degree of OSA substitution (DS) were characterized using size-exclusion chromatography and H nuclear magnetic resonance. These parameters were then correlated with viscosity, emulsification performance and antioxidant properties for OSA-stabilized oil emulsions, to gain improved understanding of structure-property relationships. The average chain length (DP) and DB respectively showed positive and negative correlations with the viscosity, total antioxidant activity (TAC), creaming extent and the emulsion z-average droplet size for all the hydrolyzed samples. The OSA-starches treated by α-amylase generally had the smallest average DP and largest DB, resulting in the lowest viscosity and the best droplet stability with the smallest creaming extent. The acid-hydrolyzed OSA-starch samples presented larger average DP than the enzyme-hydrolyzed samples, in agreement with their better TAC, while larger creaming extent. The β-amylase-hydrolyzed samples produced moderate structural degradation and emulsifying properties compared to the OSA-starches treated by α-amylase and HCl. The structure-property correlations indicate that the average chain length and DB are the two most important structural parameters in determination of the functional properties for the OSA-modified starches. These findings will help develop improved food additives with desired functions

Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high efficiency

Nanoparticle manipulation using plasmonic optical tweezers based on particle sizes and refractive indices

As an effective tool for micro/nano-scale particle manipulation, plasmonic optical tweezers can be used to manipulate cells, DNA, and macromolecules. Related research is of great significance to the development of nanoscience. In this work, we investigated a sub-wavelength particle manipulation technique based on plasmonic optical tweezers. When the local plasmonic resonance is excited on the gold nanostructure arrays, the local electromagnetic field will be enhanced to generate a strong gradient force acting on nanoparticles, which could achieve particle sorting in sub-wavelength scale. On this basis, we explored the plasmonic enhancement effect of the sorting device and the corresponding optical force and optical potential well distributions. Additionally, the sorting effect of the sorting device was investigated in statistical methods, which showed that the sorting device could effectively sort particles of different diameters and refractive indices

Femtosecond visualization of oxygen vacancies in metal oxides.

Oxygen vacancies often determine the electronic structure of metal oxides, but existing techniques cannot distinguish the oxygen-vacancy sites in the crystal structure. We report here that time-resolved optical spectroscopy can solve this challenge and determine the spatial locations of oxygen vacancies. Using tungsten oxides as examples, we identified the true oxygen-vacancy sites in WO2.9 and WO2.72, typical derivatives of WO3 and determined their fingerprint optoelectronic features. We find that a metastable band with a three-stage evolution dynamics of the excited states is present in WO2.9 but is absent in WO2.72. By comparison with model bandstructure calculations, this enables determination of the most closely neighbored oxygen-vacancy pairs in the crystal structure of WO2.72, for which two oxygen vacancies are ortho-positioned to a single W atom as a sole configuration among all O─W bonds. These findings verify the existence of preference rules of oxygen vacancies in metal oxides

Passive control of temperature distribution in cancerous tissue during photothermal therapy using optical phase change nanomaterials

Thermal therapy is a very promising alternative treatment for benign tumor, in which the temperature control is a key issue to avoid unwanted thermal damage of healthy tissue. However, the active temperature control methods usually require the assistance of real-time and accurate temperature monitoring devices. Even though, the lag of temperature control is inevitable. Therefore, in the present work, a passive control method is proposed to improve the uniformity of temperature distribution inside tumorous tissue during laser induced thermal therapy (LITT). Optical phase change nanoparticles (O-PCNPs) are utilized to replace the commonly used noble metal nanoparticles to enhance and adjust the localized light absorption in tumor. In the early stage of LITT, the O-PCNPs is used to improve the specific absorption rate in the targeted region. However, after the local temperature reaches a certain level (phase transition temperature), the O-PCNPs convert from amorphous state to crystalline state. By carefully selecting the size, shape, and laser wavelength, the absorption cross section of O-PCNPs could drop dramatically after phase transition. Therefore, in the high temperature zone the local temperature increasing rate reduces due to the reduction of local heat generation rate. On the contrary, the temperature increasing rate rises in the low temperature zone since more energy is transferred to the deeper tissue. In the present work, results show that SiO2@VO2 nanoshells can be applied as thermal contrast agents to improve the temperature uniformity in tumor during LITT

Synthetic Datasets for Autonomous Driving: A Survey

Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their expensive and time-consuming experimental and labeling costs. Therefore, more and more researchers are turning to synthetic datasets to easily generate rich and changeable data as an effective complement to the real world and to improve the performance of algorithms. In this paper, we summarize the evolution of synthetic dataset generation methods and review the work to date in synthetic datasets related to single and multi-task categories for to autonomous driving study. We also discuss the role that synthetic dataset plays the evaluation, gap test, and positive effect in autonomous driving related algorithm testing, especially on trustworthiness and safety aspects. Finally, we discuss general trends and possible development directions. To the best of our knowledge, this is the first survey focusing on the application of synthetic datasets in autonomous driving. This survey also raises awareness of the problems of real-world deployment of autonomous driving technology and provides researchers with a possible solution.Comment: 19 pages, 5 figure