Biblio

Based on the analysis of the difficulties and pain points of privacy protection in the opening and sharing of government data, this paper proposes a new method for intelligent discovery and protection of structured and unstructured privacy data. Based on the improvement of the existing government data masking process, this method introduces the technologies of NLP and machine learning, studies the intelligent discovery of sensitive data, the automatic recommendation of masking algorithm and the full automatic execution following the improved masking process. In addition, the dynamic masking and static masking prototype with text and database as data source are designed and implemented with agent-based intelligent masking middleware. The results show that the recognition range and protection efficiency of government privacy data, especially government unstructured text have been significantly improved.

The imbalance relationship between FPGA hardware/software providers and FPGA users challenges the assurance of secure design on FPGAs. Existing efforts on FPGA security primarily focus on reverse engineering the downloaded FPGA configuration, retrieving the authentication code or crypto key stored on the embedded memory in FPGAs, and countermeasures for the security threats above. In this work, we investigate new security threats from malicious FPGA tools, and identify stealthy attacks that could occur during FPGA deployment. To address those attacks, we exploit the principles of moving target defense (MTD) and propose a FPGA-oriented MTD (FOMTD) method. Our method is composed of three defense lines, which are formed by an improved user constraint file, random selection of design replicas, and runtime submodule assembling, respectively. The FPGA emulation results show that the proposed FOMTD method reduces the hardware Trojan hit rate by 60% over the baseline, at the cost of 10.76% more power consumption.

This paper presents a novel low power security system based on magnetic anomaly detection by using Tunneling Magnetoresistance (TMR) magnetic sensors. In this work, a smart light has been developed, which consists of TMR sensors array, detection circuits, a micro-controller and a battery. Taking the advantage of low power consumption of TMR magnetic sensors, the smart light powered by Li-ion battery can work for several months. Power Spectrum Density of the obtained signal was analyzed to reject background noise and improve the signal to noise ratio effectively by 1.3 dB, which represented a 30% detection range improvement. Also, by sending the signals to PC, the magnetic fingerprints of the objects have been configured clearly. In addition, the quick scan measurement has been also performed to demonstrate that the system can discriminate the multiple objects with 30 cm separation. Since the whole system was compact and portable, it can be used for security check at office, meeting room or other private places without attracting any attention. Moreover, it is promising to integrate multiply such systems together to achieve a wireless security network in large-scale monitoring.

The Internet of Things (IoT) offers a more advanced service than a single device or an isolated system, as IoT connects diverse components, such as sensors, actuators, and embedded devices through the internet. As predicted by Cisco, there will be 50 billion IoT connected devices by 2020. Integration of such a tremendous number of devices into IoT potentially brings in a new concern, system security. In this work, we review two typical hardware attacks that can harm the emerging IoT applications. As IoT devices typically have limited computation power and need to be energy efficient, sophisticated cryptographic algorithms and authentication protocols are not suitable for every IoT device. To simultaneously thwart hardware Trojan and side-channel analysis attacks, we propose a low-cost dynamic permutation method for IoT devices. Experimental results show that the proposed method achieves 5.8X higher accumulated partial guessing entropy than the baseline, thus strengthening the IoT processing unit against hardware attacks.

Controller Area Network (CAN) is the main bus network that connects electronic control units in automobiles. Although CAN protocols have been revised to improve the vehicle safety, the security weaknesses of CAN have not been fully addressed. Security threats on automobiles might be from external wireless communication or from internal malicious CAN nodes mounted on the CAN bus. Despite of various threat sources, the security weakness of CAN is the root of security problems. Due to the limited computation power and storage capacity on each CAN node, there is a lack of hardware-efficient protection methods for the CAN system without losing the compatibility to CAN protocols. To save the cost and maintain the compatibility, we propose to exploit the built-in CAN fault confinement mechanism to detect the masquerade attacks originated from the malicious CAN devices on the CAN bus. Simulation results show that our method achieves the attack misdetection rate at the order of 10-5 and reduces the encryption latency by up to 68% over the complete frame encryption method.