DocumentCode
3154548
Title
Industrial Privacy in RFID-based Batch Recalls
Author
Chaves, Leonardo Weiss Ferreira ; Kerschbaum, Florian
Author_Institution
SAP Res., Karlsruhe
fYear
2008
fDate
16-16 Sept. 2008
Firstpage
192
Lastpage
198
Abstract
Batch recalls are an important topic for manufacturers and producers. Especially in the food and in the pharmaceutical industry, producers are obliged to implement recalls in order to comply with legislation. In extreme cases, non-compliance can cause loss of life, e.g. when perished food or medicine reaches the consumer. Current batch recall practice is expensive and difficult, since many supply chain partners need to combine the data from their ERP systems. Radio frequency identification (RFID) can be used to efficiently implement batch recalls, e.g. by storing batch numbers from the parts/ingredients used in all manufacturing steps. But this raises concerns on industrial privacy, since competitors could use this information to gain insight into the whole supply chain. We overcome this problem by storing tracing information on RFID tags and encrypting the information, such that it is only available in case of a recall. We encrypt the information using identity based encryption and furthermore allow universal re-encryption along the supply chain to prevent information leakages from the ciphertexts.
Keywords
cryptography; enterprise resource planning; radiofrequency identification; supply chain management; ERP systems; RFID tags; batch numbers; batch recalls; ciphertexts; identity-based encryption; industrial privacy; information leakages; radio frequency identification; supply chain partners; tracing information; universal reencryption; Enterprise resource planning; Food industry; Food manufacturing; Identity-based encryption; Legislation; Manufacturing industries; Pharmaceuticals; Privacy; Radiofrequency identification; Supply chains;
fLanguage
English
Publisher
ieee
Conference_Titel
Enterprise Distributed Object Computing Conference Workshops, 2008 12th
Conference_Location
Munich
Print_ISBN
978-0-7695-3720-7
Type
conf
DOI
10.1109/EDOCW.2008.37
Filename
4815017
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