Abstract
A system that protects the unlinkability of certain data items (e. g. identifiers of communication partners, messages, pseudonyms, transactions, votes) does not leak information that would enable an adversary to link these items. The adversary could, however, take advantage of hints from the context in which the system operates. In this paper, we introduce a new metric that enables one to quantify the (un)linkability of the data items and, based on this, we consider the effect of some simple contextual hints.
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CITATION STYLE
Franz, M., Meyer, B., & Pashalidis, A. (2007). Attacking unlinkability: The importance of context. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4776 LNCS, pp. 1–16). Springer Verlag. https://doi.org/10.1007/978-3-540-75551-7_1
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