Towards Measuring Anonymity

By Claudia Díaz, Stefaan Seys, Joris Claessens, Bart Preneel. International Symposium on Privacy Enhancing Technologies, 2002

853 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/3edce4a9017aa823bad7c12716c67ef3248b1066

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Citations in the corpus

Robust De-anonymization of Large Sparse Datasets (2008, methodology): “In the absence of an “oracle” to identify the target record r in the lineup, the entropy of the distribution itself can be used as a metric [24, 10].”
Technical Privacy Metrics (2015, methodology): “ion of the outcome of privacy metrics, studies should always include a detailed description of the used adversary model. To this end, we extend the taxonomy of adversary types described byDiaz et al. [39] (and later refined inDiaz[38 ]), and classify adversaries as follows: 4.2.1 Local–Global. Local”
Statistical Measurement of Information Leakage (2010, methodology): “Other work in this area uses the entropy [26, 15], conditional entropy [21] and relative entropy [14].”
Verifying privacy-type properties of electronic voting protocols (2009, background): “In [20,43,12], informat ion- heoretic measures have been proposed to quantify the degree of anonymity.”
Protecting Location Privacy Through Path Confusion (2005, background): “In the context of anonymous network communication, Serjantov and Danezis [22] as well as Diaz and colleagues [11] have proposed an information theoretic metric for anonymity.”
Preserving privacy in gps traces via uncertainty-aware path cloaking (2007, methodology): “Inspired by the use of entropy in anonymous communication systems [40, 16], we use information theoretic metrics to measure uncertainty or confusion in tracking.”
An Empirical Analysis of Traceability in the Monero Blockchain (2017, methodology): “Following the approach of [7, 26] we use entropy to account for these differing probabilities.”
Security and Cooperation in Wireless Networks (2007, methodology): “in [113], by Serjantov and Danezis in [343], and by Steinbrecher and Köpsellin in [360].”
Cardinality Estimators do not Preserve Privacy (2018, methodology): “Now, Lemma 3 gives Lemma 14.”
A Constraint Satisfaction Cryptanalysis of Bloom Filters in Private Record Linkage (2011, background): “In addition to attack scenarios against privacy preserving protocols, information theoretic measures have been proposed to evaluate the degree of the privacy provided by such protocols, especially in the context of anonymous routing ([29], [30], [31]).”
How To Break Anonymity of the Netflix Prize Dataset (2006, methodology): “and use the conditional entropy of rgiven aux as the metric. In the absence of an “oracle” to identify the target record rin the lineup, the entropy of the distribution itself can be used as a metric [21, 9]. If the adversary has such an “oracle” (this is a technical device used to measure the adver”
Limits of Anonymity in Open Environments (2002, background): “However, recent research work [22, 23] limits the exponential effect on the size of the anonymity set by neglecting small probabilities, e.”
Users get routed: traffic correlation on tor by realistic adversaries (2013, background): “[12] independently propose evaluation frameworks that quantify anonymity using Shannon entropy computed over a set of potential senders (or receivers).”
Evaluating User Privacy in Bitcoin (2013, background): “In [12, 17] the authors further introduce entropy-based metrics to assess the communication privacy in anonymous networks.”
Quantifying and protecting location privacy (2015, neutral): “In specific areas, several contributions have been made to quantify privacy, be it for databases [8], for anonymityprotocols [4], or for anonymizationnetworks [7, 20, 27, 28].”