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Dr. Liu's Research

Trust and Privacy in Online Social Media

The prosperity of online social networks makes it much easier and more convenient for individual users to proactively generate, share and exchange diverse digital content online. This huge amount and rapidly growing data are playing an increasingly important role in influencing the general public’s daily lives and decisions. However, it has also raised many security, trust, and privacy issues.

First, the simplicity of creating arbitrary digital content online by any individual user has led to an increase in users' uncertainty in providing trustworthy information. More important, driven by the huge profits behind the big data economy, various malicious attacks are emerging rapidly aiming to mislead the general public’s opinions and decisions. 

Take an online reputation system as an example. In such systems, (e.g., Amazon rating platforms), users can provide their ratings and reviews based on their own experiences, and later other users can refer to those ratings and reviews to make their online purchasing decisions. Many companies aggressively boost their reputation by manipulating these online ratings either by themselves or through some “rating companies”. For example, Taobao has identified these rating boosting services (“water army”) as a severe threat.

Second, the content generated by users may explicitly or implicitly contain personal identity and relationship information, which can be used for social networking phishing, spams, and etc., raising great concerns on user privacy.

A typical example is the exposure of users' friendship in online social networks. To encourage users' social connections, popular online social networks, such as Facebook or Twitter, have recently provided a friend search engine, which allows any individual users to query another user's friendlist. Meanwhile, to address privacy concerns, social networks allow each individual user to set his/her entire friend list as “private", which supposed to be unsearchable by any other users. However, even with such settings enabled, individual users’ friendship information can still be breached easily.

My research in this direction aims to protect a healthy and trustworthy online social environment where trustworthy information can be boosted while dishonest and hostile information is suppressed.

Secure Internet of Things

With the growing prevalence of the Internet of Things (IoT), securing the sheer abundance of devices is critical. More importantly, many of these IoT devices are battery-based devices with very limited computing resources, such as CPU, memory, and storage, making it challenging to afford the high computational requirements from conventional security solutions. Our research in this direction mainly focuses on energy-efficient security solutions for resource-constrained IoT, machine-learning-based malicious network traffic analysis, and trustworthy fog computing.

 

 

 

 

Trustworthy occupancy detection model from economic sensors for building energy saving

Buildings are responsible for 70% of electricity consumption, and 48% of the total energy consumption in the US. Recent advancements in Cyber-Physical Systems (CPS) expanded opportunities to engage more people into energy-saving practices. However, most of the current functions of available smart home products are limited to remote controllability of an energy device and lack of data-based automatic learning/controlling. In addition, the sensors involved in most of these products (e.g. camera or audio sensors)are expensive and intrusive, raising great consumers’ concerns oncosts and privacy. Therefore, the goal of this project is to achieve automatic energy saving by developing a trustworthy learning model to determine occupancy information in a residential building from multiple economic and non-intrusive sensors.