Shell Game: Randomized representative based election to defend against 51% attacks in Crowd sensing frameworks

Thesis (Master's)--University of Washington, 2019Smart devices and wearable have become an epicenter of human lives and have increasingly become more complex and powerful to make people’s life easier. Smart devices like smart phones and wearable like a smart watch today are equipped to provide pervasive connectivity, quality communication and a glut of other services made possible by an array of high-grade sensors like ambient light sensor, proximity sensor, barometer and gyroscope to name a few. This unique coupling between sensor technology and human interaction has a potential to offer a multitude of opportunities and applications in mobile crowd sensing paradigm, such as real-time road traffic monitoring, noise pollution, health monitoring etc. In this paradigm, people become the centerpiece of the sensing process where users can gather data whenever and wherever, using the mobile sensor devices and they own the process of data retrieval and maintaining of the cleanliness of the data. But humans may be unreliable and malevolent and can affect the cleanliness of the data being collected for their own benefit, which is why mechanisms for detecting and deterring malevolent activities in mobile crowd sensing become imperative than ever. This paper presents a unique and efficient fabric for impeding activities like 51% attack, maintaining the integrity of the data and reduce monetary loss for the data aggregator during such attacks. This has been achieved by implementing a moving target defense in a Randomized representative based election with a proof of stake payment mechanism. To test this method, we simulate an attack by an adversary who gives malicious data and assess their total gain and the percentage of adversary presence needed to obtain a profit