Zoe L. Jiang

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.

Fully Secure Ciphertext-Policy Attribute Based Encryption with Security Mediator

Attribute-Based Encryption ABE offers fine-grained decryption policy such that users can do decryption if their attributes satisfy the policy. Such flexibility enables it applicable in various applications in government and business. However, there are two issues that should be solved first before it is deployed in practice, namely user revocation and decryption outsourcing. In this paper, we adopt the slightly modified Lewko et al.s fully-CCA-secure Ciphertext-Policy-ABE CP-ABE combining with Boneh et al.s idea of mediated cryptography to propose a CP-ABE with SEcurity Mediator SEM supporting immediate user revocation. At the same time, by the introduce of SEM, we intendedly outsource most of the computation workload in decryption to SEM side and leave only one exponentiation and one division at user side for decryption. It is proved fully-RCCA-CCA-secure in random oracle model.

Improving Disk Sector Integrity Using 3-dimension Hashing Scheme

To keep the evidence that a stored hard disk does not modify its content, the intuitive scheme is to calculate a hash value of the data in all the sectors in a specific order. However, since one or more sectors, with some probability, may become a bad sector after some time, this scheme fails to prove the integrity of all other sectors that are still good. In this paper, we suggest a scheme which calculates three hash values for each sector, in a three dimensional manner, such that the integrity proof of a sector depends only on the sectors in any one of the three dimensions, in stead of all sectors in the hard disk. Our analysis shows that this new scheme can greatly reduce the effect of bad sector formation in proving the integrity of the disk sectors.

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.

Improving Disk Sector Integrity Using K-Dimension Hashing

The integrity of data stored on a hard disk is typically verified by computing the chained hash value of disk sector data in a specific order. However, this technique fails when one or more sectors turn bad during storage, making it impossible to compute their hash values. This paper presents a k-dimension hashing scheme, which computes and stores multiple hash values for each hard disk sector. The hash values for each sector are computed in different ways; thus, when a hard disk develops bad sectors, it is still possible to verify the integrity of the data in the unaffected sectors. The paper also discusses how hashing parameters may be tuned to achieve desirable properties, including minimizing the probability that the integrity of a sector cannot be verified because other sectors have gone bad.

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.

Offline/online attribute-based encryption with verifiable outsourced decryption

In this big data era, service providers tend to put the data in a third‐party cloud system. Social networking websites are typical examples. To protect the security and privacy of the data, data should be stored in encrypted form. This brings forth new challenges: how to allow different users to access only the authorized part of the data without decryption of the data. Attribute‐based encryption (ABE) offers fine‐grained access control policy over encrypted data such that users can decrypt successfully only if their attributes satisfy the policy. However, one drawback of ABE is that the computational cost grows linearly with the complexity of ciphertext policy or the number of attributes. The situation becomes worse for mobile devices with limited computing resources. To solve this problem, we adopt the offline/online technique combining with the verifiable outsourced computation technique to propose a new ciphertext‐policy ABE scheme using bilinear groups in prime order, supporting the offline/online key generation and encryption, as well as the verifiable outsourced decryption. As a result, most computations of key generation and encryption can be executed offline, and the majority of computational workload in decryption can be outsourced to third parties. The scheme is selectively chosen‐plaintext attack‐secure in the standard model. We also provide the proof of verifiability on outsourced decryption. The simulation results show that our proposed scheme can effectively reduce the computational cost imposed on resource‐constrained devices. Copyright

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 Comparison of Moments-Based Logo Recognition Methods

Logo recognition is an important issue in document image, advertisement, and intelligent transportation. Although there are many approaches to study logos in these fields, logo recognition is an essential subprocess. Among the methods of logo recognition, the descriptor is very vital. The results of moments as powerful descriptors were not discussed before in terms of logo recognition. So it is unclear which moments are more appropriate to recognize which kind of logos. In this paper we find out the relations between logos with different transforms and moments, which moments are fit for logos with different transforms. The open datasets are employed from the University of Maryland. The comparisons based on moments are carried out from the aspects of logos with noise, and rotation, scaling, rotation and scaling.