Enabling Privacy and Trust in Edge AI Systems

Recent advances in mobile computing and the Internet of Things (IoT) enable the global integration of heterogeneous smart devices via wireless networks. A common characteristic across these modern day systems is their ability to collect and communicate streaming data, making machine learning (ML) appealing for processing, reasoning, and predicting about the environment. More recently, low network latency requirements have made offloading intelligence to the cloud undesirable. These novel requirements have led to the emergence of edge computing, an approach that brings computation closer to the device with low latency, high throughput, and enhanced reliability. Together, they enable ML-powered information processing and control pipelines spanning end devices, edge computing, and cloud environments. However, continuous collaboration between cloud, edge and device is susceptible to information leakage and loss, leading to insecure and unreliable operation. This raises an important question: how can we design, develop, and evaluate high-performing ML systems that are trustworthy and privacy-preserving in resource-constrained edge environments? In this thesis, I address this question by designing and implementing privacy-preserving and trustworthy ML systems for distributed applications. I first introduce a system that establishes trust in the explanations generated from a popular visualization technique, saliency maps, using counterfactual reasoning. Through the proposed evaluation system, I assess the degree to which hypothesized explanations correspond to the semantics of edge-based reinforcement learning environments. Second, I examine the privacy implications of personalized models in distributed mobile services by proposing time-series based model inversion attacks. To thwart such attacks, I present a distributed framework, Pelican, that learns and deploys transfer learning-based personalized ML models in a privacy preserving manner on resource-constrained mobile devices. Third, I investigate ML models that are deployed on local devices for inference and highlight the ease with which proprietary information embedded in these models can be exposed. For mitigating such attacks, I present a secure on-device application framework, SODA, which is supported by real-time adversarial detection. Finally, I present an end-to-end privacy-aware system for a real-world application to model group interaction behavior via mobility sensing. The proposed system, W4-Groups, distributes computation across device, edge, and cloud resources to strengthen its privacy and trustworthiness guarantees

Spartan Daily, April 30, 1979

Volume 72, Issue 56https://scholarworks.sjsu.edu/spartandaily/6484/thumbnail.jp

Big data in education and organizational change: Evidence from private K12 schools in China

China is a time-honored civilization with a long history of private education. In China, private education has played an important role in preserving Chinese civilization. At the end of the 20th century, private education in China began to develop thanks to government support. As such, remarkable progress was made during the past decade. Due to specific conditions within the education industry, however, the administration of private edu-cation - and basic education, in particular - has remained rudimentary compared with other more mature service industries. To address the many problems in basic education, such as rig-id teaching methods, heavy teacher workloads and long, repetitive working hours, it is imper-ative in this information era to conduct innovative explorations with the help of the “internet of things” (IoT), big data and other scientific and technological means to carry out organiza-tional reform in schools and to establish contemporary organizational structures and manage-ment modes. Doing so will comprehensively improve the administration of basic education, which will in turn promote the quality of education and teaching. This thesis examines Tianli Education Group, a typical example of private, basic educa-tion in China. By adopting experimental research methods, the behavior of students and teachers in Tianli’s schools were experimentally analyzed. IoT technology was employed to collect data about student behavior at school. Likewise, after collecting and analyzing big data on the behavior of teachers at school, the content and processes of their work were analyzed. Based on these experiments, this thesis explores a new 5G era-appropriate mode of stu-dent selection and training that makes use of big data technology. It outlines the standard work scenario for teachers and improves both their work efficiency and salaries by “trimming staff and streamlining administration,” thus rekindling enthusiasm among teachers for their work. Finally, as a part of this thesis, a series of organizational changes were implemented at Tianli Education Group and its schools to boost organizational vitality, improve overall levels of education, teaching and operational efficiency, raise teachers’ salaries and enhance student happiness.A China é uma civilização muito antiga, com uma longa história de educação privada. A educação privada desempenhou um papel importante na preservação da civilização chinesa. No final do século 20, a educação privada na China começou a desenvolver-se com o apoio do governo. Nos últimos dez anos, devido ao apoio concedido temos assistido a um grande progresso. Contudo e em virtude das condições específicas da indústria da educação, a administração da educação privada – a educação básica em particular – permaneceu rudimentar quando comparada com outras indústrias de serviços. Para resolver os muitos problemas da educação básica, tais como os métodos rígidos de ensino, as cargas de trabalho pesadas e horas de trabalho repetitivas, torna-se imperativo nesta era da informação realizar pesquisas inovadoras com a ajuda da “Internet das Coisas”, do “Big Data” e meios científicos e tecnológicos que nos permitam realizar a reforma nas escolas e estabelecer estruturas organizacionais e métodos de gestão adaptados aos tempos em que vivemos. Os resultados destas pesquisas irão contribuir para melhorar de uma forma abrangente a administração da educação básica, o que por sua vez promoverá a qualidade da educação e do ensino. Esta tese estuda o Tianli Education Group, que consideramos um bom exemplo do ensino privado na educação básica na China. Adoptando métodos experimentais de pesquisa, o comportamento dos estudantes e professores das escolas Tianli foram analisados. Aplicamos a tecnologia da “Internet das Coisas” para recolher informações sobre comportamento dos alunos na escola. Da mesma forma, após a recolha e análise dos dados sobre o comportamento dos professores na escola, efetuamos a análise do conteúdo e dos processos do seu trabalho. Tendo por base estas experiências, esta tese explora na nova era 5G, um modo apropriado para seleção e formação dos alunos. Esta tese descreve o cenário padrão de trabalho para professores e melhora não somente a eficiência do trabalho como também os seus salários ao “reduzir o pessoal e simplificar a administração”, reacendendo assim o entusiasmo dos professores pelo seu trabalho. Finalmente, como parte desta tese, uma série de mudanças organizacionais foram implementadas nas escolas do grupo Tianli Education Group com a finalidade de impulsionar a vitalidade organizacional, melhorar todos os níveis gerais de educação, aumentar a eficiência operacional e de ensino, aumentar os salários dos professores e aumentar a felicidade dos alunos

Educational anomaly analytics : features, methods, and challenges

Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field. Copyright © 2022 Guo, Bai, Tian, Firmin and Xia

Data-driven Computational Social Science: A Survey

Social science concerns issues on individuals, relationships, and the whole society. The complexity of research topics in social science makes it the amalgamation of multiple disciplines, such as economics, political science, and sociology, etc. For centuries, scientists have conducted many studies to understand the mechanisms of the society. However, due to the limitations of traditional research methods, there exist many critical social issues to be explored. To solve those issues, computational social science emerges due to the rapid advancements of computation technologies and the profound studies on social science. With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye. As a result, utilizing various data to reveal issues derived from computational social science area has attracted more and more attentions. In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human dynamics. The state-of-the-art research on human dynamics is reviewed from three aspects: individuals, relationships, and collectives. Specifically, the research methodologies used to address research challenges in aforementioned application domains are summarized. In addition, some important open challenges with respect to both emerging research topics and research methods are discussed.Comment: 28 pages, 8 figure

Personal Connections of First-Year College Students

A private, not-for-profit, 4-year urban university had been struggling to improve its 1st-year retention rate despite conducting previous studies and implementing various initiatives. This study explored the influence that students\u27 personal connections to the study site had on their experience in their 1st year in college. Tinto\u27s student integration models of attrition, Astin\u27s theory on student involvement, and Berger and Milem\u27s model of persistence served as the conceptual framework. A case study design was employed to examine faculty and staff members\u27 beliefs on how the university established and maintained connections with its students and how faculty, staff, and students viewed 1st-year initiatives and retention in relation to personal connection. Individual interviews were conducted with 3 faculty members, 3 staff members, and 15 2nd-year students. The resulting data were coded both manually and using Microsoft OneNote and were analyzed for emerging themes. Some of the results that emerged from the study included that the study site had a difficult time establishing a connection with its students, 1st-year initiatives had mixed results, students stayed at the study site because of a personal connection, and urban institutions have a difficult time establishing a connection with students. These results shed light on a new area on which the university can focus its retention and 1st-year experience efforts. A white paper was written to offer possible solutions to administrators, including changes to the dormitories and a redesign of the 1st-year seminar course. Improvements to 1st-year retention will help promote positive social change by enabling more students to stay in college and graduate on time, which in turn enhances job opportunities and the potential for higher wages

ANALYZING IMAGE TWEETS IN MICROBLOGS

Ph.DDOCTOR OF PHILOSOPH

HOW DO CONSUMERS USE SOCIAL SHOPPING WEBSITES? THE IMPACT OF SOCIAL ENDORSEMENTS

Social endorsements are user-generated endorsements of products or services, such as “likes” and personal collections, in an online social platform. We examine the effect of prior social endorsements on subsequent users’ tendency to endorse or examine a product in a social shopping context, where a social platform connect consumers and enable a collaborative shopping experience. This research consists of two parts. In part I, we identify two ways prior social endorsements can affect subsequent user behavior: as a crowd endorsement, which is an aggregate number of endorsements a product receives for anyone who comes across the product, and as a friend endorsement, which is an endorsement with the endorser’s identity delivered only to the endorser’s friends or followers. Using a panel data of 1656 products on a leading social shopping platform, we quantify the relationship between crowd and friend endorsements and subsequent examination (“click”) and endorsement (“like”) of the products, noting that examination is a private behavior while endorsement is a public behavior. Our results are consistent with the identity signaling theory where identity-conscious consumers converge with the aspiration group (the followers) in their public behavior (e.g. endorsement) and diverge from the avoidance groups (the crowd). We also find differences between public and private behaviors. Moreover, the symbolic nature of social shopping platform trumps the traditional dichotomy of symbolic/functional product attributes. Part II of this study seeks to clarify the underlying mechanism through lab experiments. We hypothesize that consumers’ evaluative attitude, specifically the value-expressive type, moderates the relationship between crowd and friend endorsements and a focal user’s product choice. Our initial results of the second study show support for this idea in the cases when the product choice is not obvious