Junbin Fang

Forensic Analysis of Pirated Chinese Shanzhai Mobile Phones

Mobile phone use – and mobile phone piracy – have increased dramatically during the last decade. Because of the profits that can be made, more than four hundred pirated brands of mobile phones are available in China. These pirated phones, referred to as “Shanzhai phones,” are often used by criminals because they are inexpensive and easy to obtain. However, the variety of pirated phones and the absence of documentation hinder the forensic analysis of these phones. This paper provides key details about the storage of the phonebook and call records in popular MediaTek Shanzhai mobile phones. This information can help investigators retrieve deleted call records and assist them in reconstructing the sequence of user activities.

An improved parameter estimation scheme for image modification detection based on DCT coefficient analysis

Most of the existing image modification detection methods which are based on DCT coefficient analysis model the distribution of DCT coefficients as a mixture of a modified and an unchanged component. To separate the two components, two parameters, which are the primary quantization step, Q1, and the portion of the modified region, α, have to be estimated, and more accurate estimations of α and Q1 lead to better detection and localization results. Existing methods estimate α and Q1 in a completely blind manner, without considering the characteristics of the mixture model and the constraints to which α should conform. In this paper, we propose a more effective scheme for estimating α and Q1, based on the observations that, the curves on the surface of the likelihood function corresponding to the mixture model is largely smooth, and α can take values only in a discrete set. We conduct extensive experiments to evaluate the proposed method, and the experimental results confirm the efficacy of our method.

Outsourcing Two-Party Privacy Preserving K-Means Clustering Protocol in Wireless Sensor Networks

Nowadays wireless sensor network (WSN) is widely used in human-centric applications and environmental monitoring. Different institutes deploy their own WSNs for data collection and processing. It becomes a challenging problem when institutes collaborate to do data mining while intend to keep data privacy on each side. Privacy preserving data mining (PPDM) is used to solve the above problem, which enables multiple parties owning confidential data to run a data mining algorithm on their combined data, without revealing any unnecessary information to each other. However, due to the huge amount of data collected and the complexity of data mining algorithms, it is preferable to outsource most of the computations to the cloud. In this paper, we consider a scenario in which two parties with weak computational power need jointly run a k-means clustering protocol, at the same time outsource most of the computation of the protocol to the cloud. As a result, each party can have the correct result calculated by the data from both parties with most of the computation outsourced to the cloud. As for privacy, the data owned by one party should be kept confidential from both the other party and the cloud.

Exposing frame deletion by detecting abrupt changes in video streams

Many existing methods for frame deletion detection attempt to detect abnormal periodical artifacts in video stream, however, due to a number of reasons, the periodical artifacts can not always be reliably detected. In this paper, we propose a new method for frame deletion detection. Rather than detecting abnormal periodical artifacts, we devise two features to measure the magnitude of variation in prediction residual and the number of intra macro blocks. Based on the devised features, we propose a fused index to capture abnormal abrupt changes in video streams. We create a dataset which consists of 6 subsets, and test the detection capability of our method in both video level and GOP (Group of Pictures) level. The experimental results show that the proposed method performs stably under various configurations.

Hard Disk Integrity Check by Hashing with Combinatorial Group Testing

In this paper, we describe the problem of verifying the integrity of a hard disk especially for forensics investigation after the computer of a suspect has been seized. Existing solutions do not provide a satisfactory solution to solve the problem. They either require a huge amount of storage to store the hash values of the sectors or may not be able to cope with the situation in an effective way in case some sectors have been modified (e.g. become bad sectors or deleted due to being part of the Legal Professional Privilege items). We introduce to use Thierry- Mieg(15)s combinatorial group testing scheme, which seems to be an unrelated topic, to design a scheme to compute the hash values for the sectors of a hard disk. The storage for hash values in our scheme can be significantly fewer than the best existing solution while requiring similar amount of execution time. And our scheme can accurately point out the sectors which have been modified while existing solutions cannot guarantee this.

High-Speed Indoor Navigation System based on Visible Light and Mobile Phone

Visible light positioning (VLP) is widely believed to be a cost-effective answer to the growing demand for real-time indoor positioning. However, due to the high computational cost of image processing, most existing VLC-based systems fail to deliver satisfactory performance in terms of positioning speed and accuracy, both of which are crucial for the performance of indoor navigation. This paper proposes a novel VLP solution that provides accurate and high-speed indoor navigation via the designs of an elaborate flicker-free line coding scheme and a lightweight image processing algorithm. In addition, this solution has the advantage of supporting flicker mitigation and dimming, which are important for illumination. An Android-based system prototype has been developed for field tests on an off-the-shelf smartphone. Experimental results show that it supports indoor positioning for users moving at a speed of up to 18 km/h. In addition, it can achieve a high accuracy of 7.5 cm, and the computational time is reduced to 22.7 ms for single-luminaire and to 35.7 ms for dual-luminaries, respectively.

Efficient Privacy-Preserving Charging Station Reservation System for Electric Vehicles

In this paper, we propose a privacy-preserving reservation system for electric vehicles (EV) charging stations. Due to the short driving range of EV, frequent charging is necessary. A mechanism for charging station reservation for EV owners is desirable. Our proposed system allows the vehicle owner to reserve a number of charging stations along the intended route at different time-slots. Yet it is secure against misuse such that a user can only hold a limited number of reservations simultaneously. More importantly, our system can provide privacy for users. The charging station does not know the identity of the user who has reserved it. Thus location privacy can be protected. We demonstrate the practicality of our system with a prototype implementation on a smart phone. Finally, we also provide a security proof to show that our system is secure under well-known computational assumptions.

A Survey on the Cyber Attacks Against Non-linear State Estimation in Smart Grids

It is well-known that critical infrastructures would be targets for cyber attacks. In this paper, we focus on smart grids. In a smart grid system, information from smart meters would be used to perform a state estimation in real time in order to maintain the stability of the system. A wrong estimation can lead to diastrous consequences e.g. suspension of electricity supply or a big financial loss. Unfortunately, quite a number of recent results showed that attacks on this estimation process are feasible by manipulating readings of only a few meters. In this paper, we focus on nonlinear state estimation which is a more realistic model and widely employed in a real smart grid environment. We summarize and categorize all possible attacks, and review the mechanisms behind. We also briefly talk about the countermeasures. We hope that the community would be able to come up with a better protection scheme for smart grids.

Sensitive Surface Plasmon Resonance biosensor based on a photonic crystal and bimetallic configuration

In this paper, we present and numerically demonstrate a highly sensitive photonic crystal (PC) Surface Plasmon Resonance (SPR) sensor. This proposed sensor consists of an Ag-Au bimetallic configuration that integrates the better evanescent field enhancement of silver, the higher resolution in the reflectivity minimum of silver, and the high chemical of gold. In comparison to most convenient SPR sensors which use single gold films, this PC-bimetallic configuration is shown theoretically to possess a narrower resonance width, smaller minimum reflectance and higher sensitivity, which makes it a much better choice to be employed for SPR biosensing applications.

A survey on cyber attacks against nonlinear state estimation in power systems of ubiquitous cities

Abstract It is well-known that critical infrastructures would be targets for cyber attacks. In this paper, we focus on the power systems (i.e. smart grids) in ubiquitous cities, where every meter is linked to an information network through wireless networking. In a smart grid system, information from smart meters would be used to perform a state estimation in real time to maintain the stability of the system. A wrong estimation may lead to disastrous consequences (e.g. suspension of electricity supply or a big financial loss). Unfortunately, quite a number of recent results showed that attacks on this estimation process are feasible by manipulating readings of only a few meters. In this paper, we focus on nonlinear state estimation which is a more realistic model and widely employed in a real power grid environment. We category cyber attacks against nonlinear state estimation, and review the mechanisms behind. State-of-the-art security measures to detect these attacks are discussed via sensor protection. Hope that the community would be able to come up with a secure system architecture for ubiquitous cities.