DE-IDENTIFICATION TECHNIQUE FOR IOT WIRELESS SENSOR NETWORK PRIVACY PROTECTION - Yennun Huang; Szu-Chuang Li; Bo-Chen Tai; Chieh-Ming Chang; Dmitrii I. Kaplun; Denis N. Butusov (Faculty of Computer Science - Universitas Indonesia, 2017)
As the IoT ecosystem becoming more and more mature, hardware and software vendors are trying create new value by connecting all kinds of devices together via IoT. IoT devices are usually equipped with sensors to collect data, and the data collected are transmitted over the air via different kinds of wireless connection. To extract the value of the data collected, the data owner may choose to seek for third-party help on data analysis, or even of the data to the public for more insight. In this scenario it is important to protect the released data from privacy leakage. Here we propose that differential privacy, as a de-identification technique, can be a useful approach to add privacy protection to the data released, as well as to prevent the collected from intercepted and decoded during over-the-air transmission. A way to increase the accuracy of the count queries performed on the edge cases in a synthetic database is also presented in this research.