By Jutta Hämmerle-Uhl, Michael Pober, Andreas Uhl (auth.), Bart De Decker, Jana Dittmann, Christian Kraetzer, Claus Vielhauer (eds.)
This booklet constitutes the refereed complaints of the 14th IFIP TC 6/TC eleven overseas convention on Communications and Multimedia safety, CMS 2013, held in Magdeburg, Germany, in September 2013. The five revised complete papers provided including eleven brief papers, five prolonged abstracts describing the posters that have been mentioned on the convention, and a pair of keynote talks have been conscientiously reviewed and chosen from 30 submissions. The papers are prepared in topical sections on biometrics; utilized cryptography; electronic watermarking, steganography and forensics; and social community privateness, protection and authentication.
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Additional resources for Communications and Multimedia Security: 14th IFIP TC 6/TC 11 International Conference, CMS 2013, Magdeburg, Germany, September 25-26, 2013. Proceedings
85 and k = 10 Fig. 6. 85 and k = 10 Fig. 7. 85 for clusters grouped by MAP and ML two variants provide very similar results, but our ﬁrst set of experiments targets each method individually. For the Bayesian method we have again performed de-identiﬁcation as described previously. We have grouped k = 10 subjects into clusters, determined by their closeness according to the classiﬁcation by MAP and ML. 85. The performance of the recognition algorithms is shown in Figure 5 and is in line with the results expected due to prior experiments for Eigenface-based algorithms.
6 0 5 10 15 20 25 Rank 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50 Rank Fig. 1. Baseline performances of the algorithms (prior to de-identiﬁcation), in the ﬁrst image with the original normalization parameters, in the second image with identical normalization for all algorithms data-set generated by EBGM pre-processing. The reason is our two-fold strategy for de-identiﬁcation. Ideally, all PCA-related modiﬁcations are applied to images normalized for PCA-based methods, then the result is transformed back to the original image which is subsequently pre-processed for EBGM.
Whereas these systems focus on the discouragement of credential sharing, the system proposed in this paper also addresses abuse (after theft) prevention and informed consent. Another approach to prevent digital credentials from easily being copied or shared is by embedding them inside tamperproof hardware. The system proposed in  leverages the DAA  protocol, available in Trusted Platform Modules (TPMs) of modern workstations, to bind the user’s anonymous credentials to a TPM. Similarly, smart cards are used to implement anonymous credential systems [2,21,1].