Access and privacy control enforcement in RFID middleware systems: Proposal and implementation on the Fosstrak platform

International audienceRadio Frequency IDentification (RFID) technology offers a new way of automating the identification and storing of information in RFID tags. The emerging opportunities for the use of RFID technology in human centric applications like monitoring and indoor guidance systems indicate how important this topic is in term of privacy. Holding privacy issues from the early stages of RFID data collection helps to master the data view before translating it into business events and storing it in databases. An RFID middleware is the entity that sits between tag readers and database applications. It is in charge of collecting, filtering and aggregating the requested events from heterogeneous RFID environments. Thus, the system, at this point, is likely to suffer from parameter manipulation and eavesdropping, raising privacy concerns. In this paper, we propose an access and privacy controller module that adds a security level to the RFID middleware standardized by the EPCglobal consortium. We provide a privacy policy-driven model using some enhanced contextual concepts of the extended Role Based Access Control model, namely the purpose, the accuracy and the consent principles. We also use the provisional context to model security rules whose activation depends on the history of previously performed actions. To show the feasibility of our privacy enforcement model, we first provide a proof-of-concept prototype integrated into the middleware of the Fosstrak platform, then evaluate the performance of the integrated module in terms of execution time

Are RNGs Achilles’ heel of RFID Security and Privacy Protocols ?

Security and privacy concerns have been growing with the increased usage of the RFID technology in our daily lives. To mitigate these issues, numerous privacy-friendly authentication protocols have been published in the last decade. Random number generators (RNGs) are commonly used in RFID tags to provide security and privacy of RFID protocols. RNGs might be weak spot of a protocol scheme and misusing of RNGs causes security and privacy problems. However, having a secure RNG with large entropy might be a trade-off between security and cost for low-cost RFID tags. Furthermore, a RNG used in RFID tag may not work properly in time. Therefore, we claim that vulnerability of using a RNG may deeply influence the security and privacy level of the system. To the best of our knowledge, this concern has not been considered in RFID literature. Motivated by this need, in this study, we first revisit Vaudenay\u27s privacy model which combines the early models and presents a new mature and elegant privacy model with different adversary classes. Then, we enhance the model by introducing a new oracle, which allows analyzing the usage of RNGs in RFID protocols. We also analyze a couple of proposed protocols under our improved model

A New Framework for RFID Privacy

Formal RFID security and privacy frameworks are fundamental to the design and analysis of robust RFID systems. In this paper, we develop a new definitional framework for RFID privacy in a rigorous and precise manner. Our framework is based on a zero-knowledge (ZK) formulation [7, 5] and incorporates the notions of adaptive completeness and mutual authentication. We provide meticulous justification of the new framework and contrast it with existing ones in the literature. In particular, we prove that our framework is stronger than the ind-privacy model of [14], which answers an open question posed in [14] for developing stronger RFID privacy models. Along the way we also try to clarify certain confusions and rectify several defects in the existing frameworks. Based on the protocol of [16], we propose an efficient RFID mutual authentication protocol and analyze its security and privacy. The methodology used in our analysis is of independent interest and can be applied to analyze other RFID protocols within the new framework

Preserving Data Privacy and Information Usefulness for RFID Data Publishing

Radio-Frequency IDentification (RFID) is an emerging technology that employs radio waves to identify, locate, and track objects. RFID technology has wide applications in many areas including manufacturing, healthcare, and transportation. However, the manipulation of uniquely identifiable objects gives rise to privacy concerns for the individuals carrying these objects. Most previous works on privacy-preserving RFID technology, such as EPC re-encryption and killing tags, have focused on the threats caused by the physical RFID tags in the data collection phase, but these techniques cannot address privacy threats in the data publishing phase, when a large volume of RFID data is released to a third party. We explore the privacy threats in RFID data publishing. We illustrate that even though explicit identifying information, such as phone numbers and SSNs, is removed from the published RFID data, an attacker may still be able to perform privacy attacks by utilizing background knowledge about a target victim's visited locations and timestamps. Privacy attacks include identifying a target victim's record and/or inferring their sensitive information. High-dimensionality is an inherent characteristic in RFID data; therefore, applying traditional anonymity models, such as K -anonymity, to RFID data would significantly reduce data utility. We propose a new privacy model, devise an anonymization algorithm to address the special challenges of RFID data, and experimentally evaluate the performance of our method. Experiments suggest that applying our model significantly improves the data utility when compared to applying the traditional K -anonymity model

A Cloud-based RFID Authentication Protocol with Insecure Communication Channels

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Radio Frequency Identification (RFID) has becomea widespread technology to automatically identify objects and withthe development of cloud computing, cloud-based RFID systemsattract more research these days. Several cloud-based RFIDauthentication protocols have been proposed to address privacyand security properties in the environment where the cloudprovider is untrusted therefore the tag’s data are encrypted andanonymously stored in the cloud database. However, most of thecloud-based RFID authentication protocols assume securecommunication channels between the reader and the cloud server.To protect data transmission between the reader and the cloudserver without any help from a third party, this paper proposes acloud-based RFID authentication protocol with insecurecommunication channels (cloud-RAPIC) between the reader and the cloud server. The cloud-RAPIC protocol preserves tag privacyeven when the tag does not update its identification. The cloudRAPIC protocol has been analyzed using the UPriv model andAVISPA verification tool which have proved that the protocolpreserves tag privacy and protects data secrecy

Preserving Privacy and Utility in RFID Data Publishing

Radio Frequency IDentification (RFID) is a technology that helps machines identify objects remotely. The RFID technology has been extensively used in many domains, such as mass transportation and healthcare management systems. The collected RFID data capture the detailed movement information of the tagged objects, offering tremendous opportunities for mining useful knowledge. Yet, publishing the raw RFID data for data mining would reveal the specific locations, time, and some other potentially sensitive information of the tagged objects or individuals. In this paper, we study the privacy threats in RFID data publishing and show that traditional anonymization methods are not applicable for RFID data due to its challenging properties: high-dimensional, sparse, and sequential. Our primary contributions are (1) to adopt a new privacy model called LKC-privacy that overcomes these challenges, and (2) to develop an efficient anonymization algorithm to achieve LKC-privacy while preserving the information utility for data mining

Analysis and Construction of Efficient RFID Authentication Protocol with Backward Privacy

Privacy of RFID systems is receiving increasing attentions in the RFID community and an important issue required as to the security of RFID system. Backward privacy means the adversary can not trace the tag later even if he reveals the internal states of the tag sometimes before. In this paper, we analyze two recently proposed RFID authentication schemes: Randomized GPS and Randomized Hashed GPS scheme. We show both of them can not provide backward privacy in Juels and Weis privacy model, which allows the adversary to know whether the reader authenticates the tag successfully or not. In addition, we present a new protocol, called Challenge-Hiding GPS, based on the Schnorr identification scheme. The challenge is hidden from the eavesdropping through the technique of Diffie-Hellman key agreement protocol. The new protocol can satisfy backward privacy, and it has less communication overheads and almost the same computation, compared with the two schemes analyzed

Privacy Protection on RFID Data Publishing

Radio Frequency IDentification (RFID) is a technology of automatic object identification. Retailers and manufacturers have created compelling business cases for deploying RFID in their supply chains. Yet, the uniquely identifiable objects pose a privacy threat to individuals. In this paper, we study the privacy threats caused by publishing RFID data. Even if the explicit identifying information, such as name and social security number, has been removed from the published RFID data, an adversary may identify a target victim's record or infer her sensitive value by matching a priori known visited locations and time. RFID data by its nature is high-dimensional and sparse, so applying traditional k -anonymity to RFID data suffers from the curse of high-dimensionality, and results in poor information usefulness. We define a new privacy model and develop an anonymization algorithm to accommodate special challenges on RFID data. Then, we evaluate its effectiveness on synthetic data sets

A Survey of RFID Authentication Protocols Based on Hash-Chain Method

Security and privacy are the inherent problems in RFID communications. There are several protocols have been proposed to overcome those problems. Hash chain is commonly employed by the protocols to improve security and privacy for RFID authentication. Although the protocols able to provide specific solution for RFID security and privacy problems, they fail to provide integrated solution. This article is a survey to closely observe those protocols in terms of its focus and limitations.Comment: Third ICCIT 2008 International Conference on Convergence and Hybrid Information Technolog

Cryptanalysis of two mutual authentication protocols for low-cost RFID

Radio Frequency Identification (RFID) is appearing as a favorite technology for automated identification, which can be widely applied to many applications such as e-passport, supply chain management and ticketing. However, researchers have found many security and privacy problems along RFID technology. In recent years, many researchers are interested in RFID authentication protocols and their security flaws. In this paper, we analyze two of the newest RFID authentication protocols which proposed by Fu et al. and Li et al. from several security viewpoints. We present different attacks such as desynchronization attack and privacy analysis over these protocols.Comment: 17 pages, 2 figures, 1 table, International Journal of Distributed and Parallel system