A hybrid multi-group approach for privacy-preserving data mining

In this paper, we propose a hybrid multi-group approach for privacy preserving data mining. We make two contributions in this paper. First, we propose a hybrid approach. Previous work has used either the randomization approach or the secure multi-party computation (SMC) approach. However, these two approaches have complementary features: the randomization approach is much more efficient but less accurate, while the SMC approach is less efficient but more accurate. We propose a novel hybrid approach, which takes advantage of the strength of both approaches to balance the accuracy and efficiency constraints. Compared to the two existing approaches, our proposed approach can achieve much better accuracy than randomization approach and much reduced computation cost than SMC approach. We also propose a multi-group scheme that makes it flexible for the data miner to control the balance between data mining accuracy and privacy. This scheme is motivated by the fact that existing randomization schemes that randomize data at individual attribute level can produce insufficient accuracy when the number of dimensions is high. We partition attributes into groups, and develop a scheme to conduct group-based randomization to achieve better data mining accuracy. To demonstrate the effectiveness of the proposed general schemes, we have implemented them for the ID3 decision tree algorithm and association rule mining problem and we also present experimental results.

A novel vector-space-based lightweight privacy-preserving RFID authentication protocol for IoT environment

The Journal of Supercomputing - Internet of Things (IoT) is a novel paradigm that connects several physical devices and the cyber world over the Internet. IoT technology is growing rapidly and soon...     

一种改进的射频识别多标签抗冲突算法

基于帧时隙Aloha算法,针对目标识别和跟踪等特殊应用中阅读器需要对其阅读范围内的标签进行反复识别的要求,根据首轮识别过程中时隙碰撞率、空闲率的值来动态调整帧长度并将调整后的帧长度的值记录下来用于阅读器的后续查询过程,由此提出了一种改进的多标签抗冲突算法。通过仿真实验表明,该算法可以更加高效快速的识别标签,具有很好的应用前景。     

RFID标签所有权转移协议研究

RFID标签在其使用过程中会涉及到所有权转移的问题,针对于此,设计出一种满足安全需求的所有权转移协议。设计出的协议不依赖可信第三方,能够减少通信实体数;采用挑战响应机制,标签的新所有者端引入计数器[count],根据[count]的值进行相对应的操作,解决去同步化攻击问题,设计出的协议能够满足标签所有权转移需要的安全要求。     

适用于低成本标签的移动RFID认证协议

移动RFID系统中,阅读器与服务器之间的通道安全假设不再成立,针对这种情况,分析了当前移动RFID认证协议的安全及性能问题,建立了移动RFID安全隐私模型;基于该模型,在兼容EPC Class-1 Generation-2低成本标签系统的基础上,提出了一种能够抵抗假冒攻击、去同步化攻击,且提供前向安全隐私保护的双向认证协议;通过安全性证明与性能比较分析,表明该协议达到了设计目的,可适用于较大规模的低成本标签移动RFID系统。     

A heuristic approach for allocation of data to RFID tags: A data allocation knapsack problem (DAKP)

Durable products and their components are increasingly being equipped with one of several forms of automatic identification technology such as radio frequency identification (RFID). This technology enables data collection, storage, and transmission of product information throughout its life cycle. Ideally all available relevant information could be stored on RFID tags with new information being added to the tags as it becomes available. However, because of the finite memory capacity of RFID tags along with the magnitude of potential lifecycle data, users need to be more selective in data allocation. In this research, the data allocation problem is modeled as a variant of the nonlinear knapsack problem. The objective is to determine the number of items to place on the tag such that the value of the “unexplained” data left off the tag is minimized. A binary encoded genetic algorithm is proposed and an extensive computational study is performed to illustrate the effectiveness of this approach. Additionally, we discuss some properties of the optimal solution which can be effective in solving more difficult problem instances.     

一种适合于低成本标签的RFID双向认证协议

在分析现有一些RFID认证协议的基础上,提出了一种新的适合低成本标签的双向认证协议,并对其进行了SMV模型检测形式化证明和性能分析。结果表明该认证协议具有认证性、保密性和完整性,能够满足低成本标签的安全需求,并且在安全性能提高的同时仍具有较好的执行性能。     

标签运动状态下的RFID系统反碰撞算法

针对现实应用中标签经常处于运动状态的情况,对传送带模型下的无线射频识别(RFID)系统进行了分析,并使用Matlab做了相应的仿真实验。结果证明,当标签密度D与传送带速度V保持不变时,识别率P在帧长度N与作用范围内未识别标签数n相等时最高。当时隙持续时间一定时,识别率P只与V和D有关,而与作用区域内传送带长度L无关。同时针对该模型,对现有的标签估算方法进行了修正,在此基础上提出了一种新的估算方法,该方法的准确度在标签数目较大时有着明显的提高。     

A dual band dual antenna with read range enhancement for UHF RFID tags

This article presents a novel dual antenna structure for dual ultra high frequency bands (f₁ = 866 MHz and f₂ = 915 MHz) for radio frequency identification tags. The proposed structure consists of two dual band antennas, one acting as a receiving antenna and the other as a backscattering antenna at both the frequency bands. The receiving antenna is designed to have input impedance complex conjugate to the impedance of tag IC in order to maximize power transfer between the antenna and the microchip. The backscattered antenna is designed to have real‐valued input impedance at both the operating frequency bands to obtain maximum differential radar cross section leading to read range enhancement. The dual band receiving antenna is designed by embedding a pair of thin slits at a radiating edge of inset fed microstrip antenna. The backscattering antenna is comprised of two elements, one is a comb‐shaped open ring element, and the other is a meander line structure which is within the open ring element. Compared to conventional antennas, the proposed dual antenna structure provides a read range enhancement due to improved maximum differential RCS. The proposed dual antenna produced 4.3 m and 6.8 m read range at 866 MHz and 915 MHz, respectively.     

A new lightweight RFID grouping authentication protocol for multiple tags in mobile environment

Multimedia Tools and Applications - Radio Frequency Identification (RFID) is a promising technology in logistics management of mobile environment. Due to the practical prospects of low-cost RFID...     

BlindIdM: A privacy-preserving approach for identity management as a service

Identity management is an almost indispensable component of today’s organizations and companies, as it plays a key role in authentication and access control; however, at the same time, it is widely recognized as a costly and time-consuming task. The advent of cloud computing technologies, together with the promise of flexible, cheap and efficient provision of services, has provided the opportunity to externalize such a common process, shaping what has been called Identity Management as a Service (IDaaS). Nevertheless, as in the case of other cloud-based services, IDaaS brings with it great concerns regarding security and privacy, such as the loss of control over the outsourced data. In this paper, we analyze these concerns and propose BlindIdM, a model for privacy-preserving IDaaS with a focus on data privacy protection. In particular, we describe how a SAML-based system can be augmented to employ proxy re-encryption techniques for achieving data confidentiality with respect to the cloud provider, while preserving the ability to supply the identity service. This is an innovative contribution to both the privacy and identity management landscapes.     

基于标签分组的RFID防碰撞算法

针对无线射频识别技术中的读写碰撞问题提出了一种新的标签防碰撞算法。该算法利用陪集分解理论将电子标签分组,限定每组标签在规定的时隙响应。根据查询码和碰撞特征位,阅读器可在一个时隙内识别出一组标签。Matlab仿真结果表明,在多标签时该算法的时隙利用率和吞吐率优于二进制搜索算法和动态帧时隙算法。     

A hybrid Arabic POS tagging for simple and compound morphosyntactic tags

The objective of this work is to develop a POS tagger for the Arabic language. This analyzer uses a very rich tag set that gives syntactic information about proclitic attached to words. This study employs a probabilistic model and a morphological analyzer to identify the right tag in the context. Most published research on probabilistic analysis uses only a training corpus to search the probable tags for each words, and this sometimes affects their performances. In this paper, we propose a method that takes into account the tags that are not included in the training data. These tags are proposed by the Alkhalil_Morpho_Sys analyzer (Bebah et al. 2011). We show that this consideration increases significantly the accuracy of the morphosyntactic analysis. In addition, the adopted tag set is very rich and it contains the compound tags that allow analyze the proclitics attached to words.     

无芯片RFID多标签防碰撞研究

针对无芯片RFID多标签难以准确识别的问题,提出一种改进型矩阵束算法(Matrix Pencil Method,MPM)解决其碰撞问题。标签散射场信号存在早时和后时响应,散射场信号极点在早时响应期间处于不稳定状态,但在后时响应期间趋于稳定。算法利用散射场信号这一特性,在多标签散射场信号中应用改进型矩阵束算法,根据后时响应开启时间和极点稳定程度的不同来区分各个标签。实验中采用多个方形开槽结构标签来建立模型。仿真结果表明,该算法能够区分各个无芯片RFID标签,基本满足了对多标签检测准确性、稳定性等方面的要求。     

一种新的RFID标签数目估算方法

动态帧时隙ALOHA反碰撞算法中帧长度调整的关键在于对阅读器读写范围内标签数目的估算。通过模拟帧时隙ALOHA算法得到了不同帧长度时标签数目与碰撞时隙数目的关系曲线。创建了碰撞时隙中的平均标签数目e和碰撞时隙所占比例P_C之间关系的数学表达式,该表达式对所有的帧长度都适用。在此基础上提出了一种新的RFID标签数目估算方法。该方法计算量小,不需要预先建立和存储查找表,有利于提高RFID系统的实时响应速度和降低硬件成本。 MATLAB仿真表明,提出的标签数目估算方法比现有的方法估算准确度有较大提高。     

Extending ECC-based RFID authentication protocols to privacy-preserving multi-party grouping proofs

Since the introduction of the concept of grouping proofs by Juels, which permit RFID tags to generate evidence that they have been scanned simultaneously, various new schemes have been proposed. Their common property is the use of symmetric-key primitives. However, it has been shown that such schemes often entail scalability, security and/or privacy problems. In this article, we extend the notion of public-key RFID authentication protocols and propose a privacy-preserving multi-party grouping-proof protocol which relies exclusively on the use of elliptic curve cryptography (ECC). It allows to generate a proof which is verifiable by a trusted verifier in an offline setting, even when readers or tags are potentially untrusted, and it is privacy-preserving in the setting of a narrow-strong attacker. We also demonstrate that our RFID grouping-proof protocol can easily be extended to use cases with more than two tags, without any additional cost for an RFID tag. To illustrate the implementation feasibility of our proposed solutions, we present a novel ECC hardware architecture designed for RFID.     

A k-anonymity privacy-preserving approach in wireless medical monitoring environments

With the proliferation of wireless sensor networks and mobile technologies in general, it is possible to provide improved medical services and also to reduce costs as well as to manage the shortage of specialized personnel. Monitoring a person’s health condition using sensors provides a lot of benefits but also exposes personal sensitive information to a number of privacy threats. By recording user-related data, it is often feasible for a malicious or negligent data provider to expose these data to an unauthorized user. One solution is to protect the patient’s privacy by making difficult a linkage between specific measurements with a patient’s identity. In this paper we present a privacy-preserving architecture which builds upon the concept of k-anonymity; we present a clustering-based anonymity scheme for effective network management and data aggregation, which also protects user’s privacy by making an entity indistinguishable from other k similar entities. The presented algorithm is resource aware, as it minimizes energy consumption with respect to other more costly, cryptography-based approaches. The system is evaluated from an energy-consuming and network performance perspective, under different simulation scenarios.     

A distributed approach to enabling privacy-preserving model-based classifier training

This paper proposes a novel approach for privacy-preserving distributed model-based classifier training. Our approach is an important step towards supporting customizable privacy modeling and protection. It consists of three major steps. First, each data site independently learns a weak concept model (i.e., local classifier) for a given data pattern or concept by using its own training samples. An adaptive EM algorithm is proposed to select the model structure and estimate the model parameters simultaneously. The second step deals with combined classifier training by integrating the weak concept models that are shared from multiple data sites. To reduce the data transmission costs and the potential privacy breaches, only the weak concept models are sent to the central site and synthetic samples are directly generated from these shared weak concept models at the central site. Both the shared weak concept models and the synthetic samples are then incorporated to learn a reliable and complete global concept model. A computational approach is developed to automatically achieve a good trade off between the privacy disclosure risk, the sharing benefit and the data utility. The third step deals with validating the combined classifier by distributing the global concept model to all these data sites in the collaboration network while at the same time limiting the potential privacy breaches. Our approach has been validated through extensive experiments carried out on four UCI machine learning data sets and two image data sets.

关于RFID标签的安全策略研究

在比较分析现有的RFID标签安全策略基础上,提出一种新型的基于随机更新过程的安全策略。该策略能有效解决RFID标签信息的安全问题,并且不需要高强度运算和加密技术。该策略在每次标签激活时由标签内电路随机改变其数据并向应用系统发出通知。应用系统接收到通知后更新已注册的标签数据。经过多次激活之后,标签新数据与旧数据完全不同,从而防止了标签非法跟踪。介绍不可链接性的定义,并在相关理论的基础上给出详细的分析。     

RFID系统标签防碰撞的研究与改进

RFID系统中标签碰撞是一个常见问题。为解决该问题,基于Gen-2协议提出了一种改进算法,该算法考虑到阅读器对碰撞时隙和空闲时隙的响应时间不同,运用两个不同的C值动态调整Q值,并在碰撞的情况下对时隙进行局部调整,减少了标签的识别通信次数,提高了系统吞吐率,并通过仿真得到验证。