Lin Li

Bio: Lin Li is an academic researcher from Prairie View A&M University. The author has contributed to research in topics: Internet privacy & Business. The author has an hindex of 1, co-authored 3 publications receiving 13 citations.

Voice Controlled Smart Mirror with Multifactor Authentication

Abstract: Internet of Things, Smart Home, and Smart Cities have become hot topics in recent years. The development of these areas grows spirally due to the emergence of advanced smart devices. Smart mirrors are a new addition to the smart home family that has been getting a lot of attention nowadays by both commercial manufacturers and academia. This paper describes how a Raspberry Pi device can be used to enhance such mirrors with intelligence and security. The goal is to develop a cost effective intelligent mirror system that not only works as a regular mirror, but also be able to display various kinds of information, such as weather, time and location, current events, and users. The mirror can provide multimedia services while ensuring high end security across the entire system.

A labware for educating location privacy protection in location-based services

Abstract: This paper discusses the design and implementation of a labware developed for teaching location privacy in Location Based Services (LBS) for undergraduate students. The objectives of developing such a labware are not only to increase students' awareness of location protection, but also to provide them with different views on this privacy problem from the perspectives of a service user, a malicious LBS, and a privacy analyst. Using this labware to teach security related classes will help students gain a comprehensive understanding of the location privacy problem in LBS. Surveys conducted among 75 students majoring in Computer Science at Prairie View A&M University showed the successful achievement of the objectives. This labware can be widely used in the classrooms and integrated into security curriculum.

FriPEL: Friendship Privacy Educational Labware

Abstract: As social media grows increasingly in popularity, so do people's concerns about their privacy disclosure. Considering the large amount of time the younger generations spend on online social networks, educational activities are needed to promote privacy protection. This paper presents an initial effort on exploring a hands-on learning approach to educate students on social media privacy. Specifically, a labware was developed to educate students that their query behaviors in online social networks may disclose the private relationships of other users on the site. The labware aims at making students aware of the privacy issues on social media, understanding the costs of privacy protection, stimulating their interests, and improving students self-efficacy. This paper discusses the design and implementation of the labware, which was evaluated among a group of student volunteers through the lecture and hands-on activities. Student feedback was very positive and encouraging.

Learning and Preserving Relationship Privacy in Photo Sharing

Abstract: In recent years, Online Social Networks (OSN) have become popular content-sharing environments. With the emergence of smartphones with high-quality cameras, people like to share photos of their life moments on OSNs. The photos, however, often contain private information that people do not intend to share with others (e.g., their sensitive relationship). Solely relying on OSN users to manually process photos to protect their relationship can be tedious and error-prone. Therefore, we designed a system to automatically discover sensitive relations in a photo to be shared online and preserve the relations by face blocking techniques. We first used the Decision Tree model to learn sensitive relations from the photos labeled private or public by OSN users. Then we defined a face blocking problem and developed a linear programming model to optimize the tradeoff between preserving relationship privacy and maintaining the photo utility. In this paper, we generated synthetic data and used it to evaluate our system performance in terms of privacy protection and photo utility loss.

Internet of Things Based Smart Mirrors: A Literature Review

Abstract: Internet of Things (IoT) allows devices to communicate with each other in different and important places at the same time. Smart things are developed in ways that interact with each other, such as smart doors and smart homes. One of the most important IoT applications is the smart mirror. It is a mirror that acts as a reflective surface and as an interactive screen at the same time. Smart mirrors can be implemented for different purposes such as a simulator for medical students and an assistant in the fitting rooms. This paper presents a review upon applications of smart mirrors.

Towards Somaesthetic Smarthome Designs: Exploring Potentials and Limitations of an Affective Mirror

Abstract: Have you ever experienced a mirror that not only "sees" how you see yourself, but tells you how others perceive you? May such a smartmirror help not only adapt/improve oneself externally (look) but also internally (mood, emotions)? What are the expectations, fears (privacy)? The acceptance of such a smart thing requires approaches that go beyond technology-driven designs. In this paper, we report on the construction of an affective emotion-sensitive smartmirror as a smart piece of furniture for smarthomes and explorative studies with that system which address somaesthetics and aims to help inhabitants in self-reflection. The paper presents and discusses the prototype implementation and the results of a study in which the ideas, concerns, and experiences for/with the self-reflection capabilities of the affective smartmirror were explored by participants using the prototype as a technology probe.

Assessing the Effectiveness of a Gamified Social Network for Applying Privacy Concepts: An Empirical Study With Teens

Abstract: The concept of privacy in online social networks (OSNs) is a challenge, especially for teenagers. Previous works deal with teaching about privacy using educational online content, and media literacy. However, these tools do not necessarily promote less risky behaviors, and do not allow the assessment of users’ behavior after the learning period. Moreover, few research studies about the effects of social gamification have been performed for this population segment (i.e., teenagers). To address this problem in this article, we propose the use of gamification in an OSN called Pesedia to facilitate the teaching/learning process, and assess its effectiveness in promoting suitable privacy behaviors. We tested our proposal comparing teenagers’ performance in two editions of a course about social networks, and privacy (with, and without gamification) for one month. We measured the impact of gamification in the participants’ behaviors toward privacy concepts as a consequence of the privacy teaching/learning process, and the participants’ engagement in the educational process. The results show that there are significant differences in participants’ behavior regarding privacy, and engagement in the gamified social network. Moreover, there is also a significant difference in participants’ engagement for the gamified male participants. The gamified social network proposed in this article may be relevant, and useful for educators who wish to develop, and enhance teenagers’ privacy skills, or for a broader base of aspects related to the development of digital competences, and technology in education.

LUX: Smart Mirror with Sentiment Analysis for Mental Comfort.

Abstract: As COVID-19 solidifies its presence in everyday life, the interest in mental health is growing, resulting in the necessity of sentiment analysis. A smart mirror is suitable for encouraging mental comfort due to its approachability and scalability as an in-home AI device. From the aspect of natural language processing (NLP), sentiment analysis for Korean lacks an emotion dataset regarding everyday conversation. Its significant differences from English in terms of language structure make implementation challenging. The proposed smart mirror LUX provides Korean text sentiment analysis with the deep learning model, which examines GRU, LSTM, CNN, Bi-LSTM, and Bi-GRU networks. There are four emotional labels: anger, sadness, neutral, and happiness. For each emotion, there are three possible interactive responses: reciting wise sayings, playing music, and sympathizing. The implemented smart mirror also includes more-typical functions, such as a wake-up prompt, a weather reporting function, a calendar, a news reporting function, and a clock.

MirrorME: implementation of an IoT based smart mirror through facial recognition and personalized information recommendation algorithm.

Abstract: We are living in the era of the fourth industrial revolution, which also treated as 4IR or Industry 4.0. Generally, 4IR considered as the mixture of robotics, artificial intelligence (AI), quantum computing, the Internet of Things (IoT) and other frontier technologies. It is obvious that nowadays a plethora of smart devices is providing services to make the daily life of humans easier. However, in the morning most people around the globe use a traditional mirror while preparing themselves for daily tasks. The aim is to build a low-cost intelligent mirror system that can display a variety of details based on user recommendations. Therefore, in this article, Internet of Things (IoT) and AI-based smart mirror is introduced that will support the users to receive the necessary daily update of weather information, date, time, calendar, to-do list, updated news headlines, traffic updates, COVID-19 cases status and so on. Moreover, a face detection method also implemented with the smart mirror to construct the architecture more secure. Our proposed MirrorME application provides a success rate of nearly 87% in interacting with the features of face recognition and voice input. The mirror is capable of delivering multimedia facilities while maintaining high levels of security within the device.