Towards an Information Theoretic Metric for Anonymity

By Andrei Serjantov, George Danezis. International Symposium on Privacy Enhancing Technologies, 2002

891 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/745449bd7ad5d32224685e9188591e53395f376e

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Citations in the corpus

AMOEBA: Robust Location Privacy Scheme for VANET (2007, background): “However, for an anonymity set with a non-uniform distribution, the entropy of the distribution was shown to be a more suitable metric for anonymity level [17], as will be illustrated in Section V-D.”
An inquiry into money laundering tools in the Bitcoin ecosystem (2013, background): “, [18], [19]), their adaptation for transaction systems like Bitcoin leave many open research questions.”
How To Break Anonymity of the Netflix Prize Dataset (2006, methodology): “This is similar to communication anonymity in mix networks [21].”
Verifying privacy-type properties of electronic voting protocols (2009, background): “In [21,44,13], informat ion- heoretic measures have been proposed to quantify the degree of 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.”
Mix-Zones for Location Privacy in Vehicular Networks (2007, background): “The location privacy of a vehicle corresponding to event l is the entropy of pk→l ([25])”
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.”
Users get routed: traffic correlation on tor by realistic adversaries (2013, background): “Later work by Murdoch and Danezis show that trafﬁc correlation attacks can be done quite efﬁciently against Tor [28].”
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].”
Achieving k-anonymity in privacy-aware location-based services (2014, methodology): “We first choose these dummy locations based on the entropy metric [12], and then enhance the algorithm, called enhanced-DLS, by making sure that the selected dummy locations are spread far away.”
Statistical Measurement of Information Leakage (2010, methodology): “Information theory provides powerful techniques to measure the relation between different probability distributions and so has proved useful for defining anonymity [26,15,24,29,9,11] and quantitative information flow [22,21,12,19,2].”
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.”
Hang with your buddies to resist intersection attacks (2013, neutral): “admirably addressed by relevant prior work [7, 26].”
From a Trickle to a Flood: Active Attacks on Several Mix Types (2002, methodology): “Assuming reasonable parameters, [13] shows that the anonymity contribution of pool messages is quantifiably greater than that of messages in a new batch.”
Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures (2010, methodology): “using information theoretic metrics, for which there is a line of active research [17], [ 18 ].”
Technical Privacy Metrics (2015, background): “of a random variable. As a privacy measure, it can be interpreted as the eective size of the anonymity set, or as the number of bits of additional information the adversary needs to identify a user [Serjantov and Danezis 2002]. The random variable Xindicates the adversary’s estimated probabilities f”
Karaoke: Distributed Private Messaging Immune to Passive Traffic Analysis (2018, background): “Loopix [20] is a recent system that delays messages and uses entropy [24] as a metric for reasoning about a user’s anonymity set.”