Date of Award


Degree Name

MS in Computer Science


Computer Science


College of Engineering


Dongfeng Fang

Advisor Department

Computer Science

Advisor College

College of Engineering


Internet of Things (IoT) devices have been widely adopted in many different applications in recent years, such as smart home applications. An adversary can capture the network traffic of IoT devices and analyze it to reveal user activities even if the traffic is encrypted. Therefore, traffic privacy is a major concern, especially in smart home applications. Traffic shaping can be used to obfuscate the traffic so that no meaningful predictions can be drawn through traffic analysis. Current traffic shaping methods have many tunable variables that are difficult to optimize to balance bandwidth overheads and latencies. In this thesis, we study current traffic shaping algorithms in terms of computational requirements, bandwidth overhead, latency, and privacy protection based on captured traffic data from a mimic smart home network. A new traffic shaping method - Dynamic Traffic Padding is proposed to balance bandwidth overheads and delays according to the type of devices and desired privacy. We use previous device traffic to adjust the padding rate to reduce the bandwidth overhead. Based on the mimic smart home application data, we verify our proposed method can preserve privacy while minimizing bandwidth overheads and latencies.