Bozhong Liu

On the security of 4-bit involutive S-boxes for lightweight designs

In this work we investigate all the 4-bit involutive S-boxes with linear, differential and almost resilient analysis. The results show that involutive S-boxes can be optimal against linear attack. We prove that for a 4-bit involutive S-box there always exists a pair of input and output differences such that the Hamming distance is 1, which does not satisfy the strict resistance on differential analysis. Moreover, we find that the almost resilient property is not effective to judge the security of 4-bit involutive S-boxes in practise. How to use the almost resilient property to set up a criterion for an optimal secure S-box needs investigations.

RCP mining: Towards the summarization of spatial co-location patterns

Co-location pattern mining is an important task in spatial data mining. However, the traditional framework of co-location pattern mining produces an exponential number of patterns because of the downward closure property, which makes it hard for users to understand, or apply. To address this issue, in this paper, we study the problem of mining representative co-location patterns (RCP). We first define a covering relationship between two co-location patterns by finding a new measure to appropriately quantify the distance between patterns in terms of their prevalence, based on which the problem of RCP mining is formally formulated. To solve the problem of RCP mining, we first propose an algorithm called RCPFast, adopting the post-mining framework that is commonly used by existing distance-based pattern summarization techniques. To address the peculiar challenge in spatial data mining, we further propose another algorithm, RCPMS, which employs the mine-and-summarize framework that pushes pattern summarization into the co-location mining process. Optimization strategies are also designed to further improve the performance of RCPMS. Our experimental results on both synthetic and real-world data sets demonstrate that RCP mining effectively summarizes spatial co-location patterns, and RCPMS is more efficient than RCPFast, especially on dense data sets.

Mining Co-locations from Continuously Distributed Uncertain Spatial Data

A co-location pattern is a group of spatial features whose instances tend to locate together in geographic space. While traditional co-location mining focuses on discovering co-location patterns from deterministic spatial data sets, in this paper, we study the problem in the context of continuously distributed uncertain data. In particular, we aim to discover co-location patterns from uncertain spatial data where locations of spatial instances are represented as multivariate Gaussian distributions. We first formulate the problem of probabilistic co-location mining based on newly defined prevalence measures. When the locations of instances are represented as continuous variables, the major challenges of probabilistic co-location mining lie in the efficient computation of prevalence measures and the verification of the probabilistic neighborhood relationship between instances. We develop an effective probabilistic co-location mining framework integrated with optimization strategies to address the challenges. Our experiments on multiple datasets demonstrate the effectiveness of the proposed algorithm.

Protecting lightweight block cipher implementation in mobile big data computing: A GPU-based approach

The Mobile Big Data Computing is a new evolution of computing technology in data communication and processing. The data generated from mobile devices can be used for optimization and personalization of mobile services and other profitable businesses. Mobile devices are usually with limited computing resources, thus the security measures are constrained. To solve this problem, lightweight block ciphers are usually adopted. However, due to the easily exposed environment, lightweight block ciphers are apt to suffer from differential power attack. To counteract this attack, Nikova et al. proposed a provably secure method, namely sharing, to protect the cipher’s implementation. But the complexity of sharing method is so high, making this method not practical. To address this issue, in this paper, we propose a GPU-based approach of sharing a 4-bit S-box by automatic search. GPU is a promising acceleration hardware with powerful parallel computing. By analyzing the sharing method carefully, we devise an optimal approach, namely OptImp, that improves the performance massively. The experiment results show that the proposed approach can achieve up to 300 times faster than the original method. With our approach, the sharing method can be used to protect lightweight block ciphers in practice.

Encrypted data indexing for the secure outsourcing of spectral clustering

Spectral clustering is one of the most popular clustering methods and is particularly useful for pattern recognition and image analysis. When using spectral clustering for analysis, users are either required to implement their own platforms, which requires strong data analytics and machine learning skills, or allow a third party to access and analyze their data, which may compromise their data privacy or security. Traditionally, this problem is solved by privacy-preserving data mining using randomization perturbation or secure multi-party computation. However, the existing methods suffer from the problems of inaccurate results or high computational requirements on the data owner’s side. To address these problems, in this paper, we propose a new secure outsourcing data mining (SODM) paradigm, which allows data owners to encrypt their data to ensure maximum data security. After the encryption, data owners can outsource their encrypted data to data analytics service providers (i.e., data analytics agent) for knowledge discovery, with a guarantee that neither the data analytics agent nor the other parties can compromise data privacy. To allow data mining to be efficiently carried out on encrypted data, we design a secure KD-tree to index all the encrypted data. Based on the SODM framework, a secure spectral clustering algorithm is proposed. The experiments on real-world datasets demonstrate the effectiveness and the efficiency of the system for the secure outsourcing of data mining.

Restrictive partially blind signature for resource-constrained information systems

Restrictive partially blind signature, which is designed for privacy-oriented information systems, allows a user to obtain a blind signature from a signer while the blind message must obey some certain rules. In order to reduce storage and communication costs, several public-key cryptosystems are constructed using characteristic sequences generated by linear feedback shift register (LFSR). In this paper, we present a new partially blind signature scheme with the restrictive property, which is based on nth order characteristic sequences generated by LFSR. By assuming the intractability of the discrete logarithm problem, our sequence-based schemes are provably secure in the random oracle model. We also present a practical e-cash application based on our restrictive partially blind signature. Due to the reduced representation of finite field elements and feasible sequence operations from LFSR, our scheme is time- and storage-efficient on both of signer and user sides. The advantages will make privacy-oriented applications more practical for resource-constrained devices.

An Impeaching System Based on Bit Commitment with Revocable Anonymity

In this paper we propose a new secure impeaching system based on bit commitment. The scheme keeps the informers privacy in ordinary routine: Once a potential malicious prosecution occurs, the anonymity will be removed by a trusted thirty party (TTP) with the cooperation from electronic impeaching center (EIC), and the malicious user can be called to account. This scheme is more efficient than previous proposals and has adaptability in various numbers of users.