INFLUENCE OF SURFACE MODIFICATION ON PROPERTIES AND APPLICATIONS OF COMPLEX ENGINEERED NANOPARTICLES

Complex engineered nanoparticles (CENPs) are being used on various applications. Their properties are different from those of neat nanoparticles. The dissertation explores these differences from four aspects: 1) Modify carbon nanomaterials’ inert surfaces and investigate the effect on thermal and rheological behavior of their dispersions; 2) Generate self-assembly bi-layer structure of oxide nanoparticles via surface modification; 3) Study interaction between lysozyme and different surface-charged ceria nanoparticles; 4) Investigate the biodistribution and transformations of CENPs in biological media. An environment-friendly surface modification was developed to modify surfaces of carbon nanomaterials for increasing their affinity to non-polar fluid. It can offset formation of agglomerates in dispersions. Less agglomerates change thermal conductivity and rheological behavior. One combined model, considering shape factor, was built to fit non-linear enhancement on thermal conductivity with volume fraction of nanoparticles. Constructing bi-layer structure of oxide nanoparticles with different refractive index was crucial for optical thin films. Silanization was used to transform relatively hydrophilic surface of oxide nanoparticles to hydrophobic surface via attaching alkane chains. The self-assembly separation of these nanoparticles can form bi-layer structure in single deposition process since neat nanoparticles keep in hydrophilic monomer while surface-modified nanoparticles settled down. The adsorption behaviors of lysozyme, one protein with net positive charge, on different surface-charged ceria nanoparticles were investigated. The adsorption isotherm curves were fitted with the Toth and Sips equations satisfactorily. The heterogeneity parameters suggest the surface charge predominate adsorption on negatively charged ceria while lateral effect predominate adsorption on positively charged ceria. The local site energy distributions were also estimated. The 26Al-labeled nanoalumina coated by 14C-labeled citrate was synthesized and its dispersion was infused intravenously into rat. The Accelerator Mass Spectrometer (AMS) was used to measure isotopes in dosing material and tissues. The ratio of coating and core in liver was slightly less than dosing material while the ratios in brain and bone are much higher than dosing material. It may suggest that some citrate coating dissociated from nanoalumina’s surface, entered metabolic cycles, and then redistributed to other organs

Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation

Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously. State-of-the-art collective classification methods for such graph-based security and privacy analytics follow the following paradigm: assign weights to edges of the graph, iteratively propagate reputation scores of nodes among the weighted graph, and use the final reputation scores to classify nodes in the graph. The key challenge is to assign edge weights such that an edge has a large weight if the two corresponding nodes have the same label, and a small weight otherwise. Although collective classification has been studied and applied for security and privacy problems for more than a decade, how to address this challenge is still an open question. In this work, we propose a novel collective classification framework to address this long-standing challenge. We first formulate learning edge weights as an optimization problem, which quantifies the goals about the final reputation scores that we aim to achieve. However, it is computationally hard to solve the optimization problem because the final reputation scores depend on the edge weights in a very complex way. To address the computational challenge, we propose to jointly learn the edge weights and propagate the reputation scores, which is essentially an approximate solution to the optimization problem. We compare our framework with state-of-the-art methods for graph-based security and privacy analytics using four large-scale real-world datasets from various application scenarios such as Sybil detection in social networks, fake review detection in Yelp, and attribute inference attacks. Our results demonstrate that our framework achieves higher accuracies than state-of-the-art methods with an acceptable computational overhead.Comment: Network and Distributed System Security Symposium (NDSS), 2019. Dataset link: http://gonglab.pratt.duke.edu/code-dat

Graph-based security and privacy analytics via collective classification

Graphs are a powerful tool to represent complex interactions between various entities. A particular family of graph-based machine learning techniques called collective classification has been applied to various security and privacy problems, e.g., malware detection, Sybil detection in social networks, fake review detection, malicious website detection, auction fraud detection, APT infection detection, attribute inference attacks, etc.. Moreover, some collective classification methods have been deployed in industry, e.g., Symantec deployed collective classification to detect malware; Tuenti, the largest social network in Spain, deployed collective classification to detect Sybils. In this dissertation, we aim to systematically study graph-based security and privacy problems that are modeled via collective classification. In particular, we focus on collective classification methods that leverage random walk (RW) or loopy belief propagation (LBP). First, we propose a local rule-based framework to unify existing RW-based and LBP-based methods. Under our framework, existing methods can be viewed as iteratively applying a different local rule to every node in the graph. know about the node. Second, we design a novel local rule for undirected graphs. Based on our local rule, we propose a collective classification method that can maintain the advantages and overcome the disadvantages of state-of-the-art undirected graph-based collective classification methods for Sybil detection. Third, many security and privacy problems are modeled using directed graphs. Directed graph- based security and privacy problems have their unique characteristics. Existing undirected graph- based collective classification methods (e.g., LBP-based methods) cannot be applied to directed graphs and existing directed graph-based methods (e.g., RW-based methods) cannot make full use of the labeled training set. To address the issue, we develop a novel local rule for directed graph-based Sybil detection and propose a collective classification method that captures unique characteristics of directed graph-based Sybil detection. Finally, one key issue of all collective classification methods is that they either assign small weights to a large number of edges whose two corresponding nodes have the same label or/and assign large weights to a large number of edges whose two corresponding nodes have different labels. Although collective classification has been studied and applied for security and privacy problems for more than a decade, it is still challenging to assign edge weights such that an edge has a large weight if the two corresponding nodes have the same label, and a small weight otherwise. We develop a novel collective classification framework to address this long-standing challenge. Specifically, we first formulate learning edge weights as an optimization problem, which, however, is computationally challenging to solve. Then, we relax the optimization problem and design an efficient joint weight learning and propagation algorithm to solve this approximate optimization problem

Advancing Data Privacy: A Novel K-Anonymity Algorithm with Dissimilarity Tree-Based Clustering and Minimal Information Loss

Anonymization serves as a crucial privacy protection technique employed across various technology domains, including cloud storage, machine learning, data mining and big data to safeguard sensitive information from unauthorized third-party access. As the significance and volume of data grow exponentially, comprehensive data protection against all threats is of utmost importance. The main objective of this paper is to provide a brief summary of techniques for data anonymization and differential privacy.A new k-anonymity method, which deviates from conventional k-anonymity approaches, is proposed by us to address privacy protection concerns. Our paper presents a new algorithm designed to achieve k-anonymity through more efficient clustering. The processing of data by most clustering algorithms requires substantial computation. However, by identifying initial centers that align with the data structure, a superior cluster arrangement can be obtained.Our study presents a Dissimilarity Tree-based strategy for selecting optimal starting centroids and generating more accurate clusters with reduced computing time and Normalised Certainty Penalty (NCP). This method also has the added benefit of reducing the Normalised Certainty Penalty (NCP). When compared to other methods, the graphical performance analysis shows that this one reduces the amount of overall information lost in the dataset being anonymized by around 20% on average. In addition, the method that we have designed is capable of properly handling both numerical and category characteristics

Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks

Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more efficient than graph neural networks. However, existing pMRF-based methods are less accurate than graph neural networks, due to a key limitation that they assume a heuristics-based constant edge potential for all edges. In this work, we aim to address the key limitation of existing pMRF-based methods. In particular, we propose to learn edge potentials for pMRF. Our evaluation results on various types of graph datasets show that our optimized pMRF-based method consistently outperforms existing graph neural networks in terms of both accuracy and efficiency. Our results highlight that previous work may have underestimated the power of pMRF for semi-supervised node classification.Comment: Accepted by AAAI 202