Privacy-preserving computation of benchmarks on item-level data using RFID

Kerschbaum, Florian; Oertel, Nina; Chaves, Leonardo Weiss Ferreira

doi:10.1145/1741866.1741884

Cited by 12 publications

(10 citation statements)

References 17 publications

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“…This is significantly more information than necessary to authenticate or make the access decision. Although our protocols already improve over proposals for standards [1] by revealing this information only to partners on the same supply chain and not any player in the system, approaches using modern cryptography [29,46] show that significantly better confidentiality protection is feasible. A more privacy-preserving protocol that only reveals the necessary information, such as the rank, seems like a welcomed improvement.…”

Section: Improvements To the Authentication Protocolmentioning

confidence: 95%

“…On the one hand, combining this data from many companies (just predecessor and successor is almost always insufficient) along the supply chain enables or improves many economically attractive collaborative applications, such as batch recalls [46], counterfeit detection [43], benchmarking and analytics [27,28,29] or estimated arrival forecasts [12]. We therefore expect an increasing interest by companies to adopt object-level tracking technology.…”

Section: Introductionmentioning

confidence: 99%

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An access control model for mobile physical objects

Kerschbaum¹

2010

Proceedings of the 15th ACM Symposium on Access Control Models and Technologies

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Access to distributed databases containing tuples collected about mobile physical objects requires information about the objects' trajectories. Existing access control models cannot encode this information efficiently. This poses a policy management problem to administrators in real-world supply chains where companies want to protect their goods tracking data. In this paper we propose a new access control model as an extension to attribute-based access control that allows trajectory-based visibility policies. We prove the security properties of our novel authentication protocol for distributed systems that can supply the decision algorithm with the necessary reliable information using only standard passive RFID tags. As a result companies will be able to improve confidentiality protection and governance of their object tracking data and more trustingly engage in data sharing agreements.

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Section: Improvements To the Authentication Protocolmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

An access control model for mobile physical objects

Kerschbaum¹

2010

Proceedings of the 15th ACM Symposium on Access Control Models and Technologies

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“…Kerschbaum et al [19] propose to privately compute performance properties of an RFID supply chain using data stored on tags. However, this work focuses on computing these metrics without leaking sensitive information of the supply-chain's parties.…”

Section: Related Workmentioning

confidence: 99%

“…However, this work focuses on computing these metrics without leaking sensitive information of the supply-chain's parties. Kerschbaum et al [19] use additive homomorphic encryption that does not support collecting statistics on multiple properties and consequently cannot be as efficient as PPS.…”

Section: Related Workmentioning

confidence: 99%

PPS: Privacy-preserving statistics using RFID tags

Blaß

Elkhiyaoui

Molva

2012

2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)

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Abstract. As RFID applications are entering our daily life, many new security and privacy challenges arise. However, current research in RFID security focuses mainly on simple authentication and privacy-preserving identification. In this paper, we widen the scope of RFID security and privacy by introducing a new application scenario. The suggested application consists of computing statistics on private properties of individuals stored in RFID tags. The main idea is to compute global statistics while preserving the privacy of individual readings. PPS assures the privacy of properties stored in each tag through the combination of homomorphic encryption and aggregation at the readers. Re-encryption is used to prevent tracking of users. The readers scan tags and forward the aggregate of their encrypted readings to the back-end server. The back-end server then decrypts the aggregates it receives and updates the global statistics accordingly. Therewith, PPS is provably privacy-preserving. Moreover, tags can be very simple since they are not required to perform any kind of computation, but only to store data.

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“…Data commonly collected in supply chains include time, location, and type of handling (e.g., packing, unpacking, receiving, or shipping). On one hand, combining these data from all the companies along a supply chain (not just predecessor and successor of each phase) enables or improves many economically attractive collaborative applications, including batch recalls [29], counterfeit detection [28], benchmarking and analytics [18]- [20], or estimated arrival forecasts [6]. On the other hand, information collected along the supply chain may be considered sensitive as it allows espionage on the business operations of the involved companies [12], [23].…”

Section: Introductionmentioning

confidence: 99%

Selective access for supply chain management in the cloud

Tueno¹,

Kerschbaum

Bernau³

et al. 2017

2017 IEEE Conference on Communications and Network Security (CNS)

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Abstract-Object-level tracking along supply chains, enabled by the low-cost and wide availability of Radio Frequency Identification (RFID) technology, permits companies to collect large amounts of data (e.g., time, location, handling) about the goods they produce. Combining the data collected by the different companies along a supply chain can provide considerable advantages to all of them. However, such a sharing sometimes needs to be selective. Indeed, companies may need to keep some information about their business operations secret. In this paper, we propose a solution that enables selective sharing of data collected along a supply chain. Our solution uses the services offered by cloud providers for sharing data among companies, and relies on selective encryption for enforcing access restrictions over such data.

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Privacy-preserving computation of benchmarks on item-level data using RFID

Cited by 12 publications

References 17 publications

An access control model for mobile physical objects

An access control model for mobile physical objects

PPS: Privacy-preserving statistics using RFID tags

Selective access for supply chain management in the cloud

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