Spelling and clarity fixes
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@ -10,14 +10,14 @@ The short answer is "it depends".
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The longer answer:
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When different parties have different false positive rates. The server can calculate the skew between a party's ideal
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When different parties have different false positive rates the server can calculate the skew between a party's ideal
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false positive rate and observed false positive rate.
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That skew leaks information, especially given certain message distributions. Specifically it leaks parties
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who receive a larger proportion of system messages than their ideal false positive rate.
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i.e. for low false positive rates and high message volume for a specific receiver, the adversarial server
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can calculate a skew that leaks the receipient of individual messages - breaking privacy for that receiver.
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can calculate a skew that leaks the recipient of individual messages - breaking privacy for that receiver.
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It *also* removes those messages from the pool of messages that an adversarial server needs to consider for other receivers.
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Effectively reducing the anonymity set for everyone else.
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@ -60,7 +60,7 @@ Under a certain threshold of parties, trivial breaks (i.e. tags that only match
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Assuming we have large number of parties (_N_), the following heuristic emerges:
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* Parties who only expect to receive a small number of messages can safely choose smaller false positive rates, up
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to a threshold _θ_, where θ > 2^-N. The lower the value of _θ_ the greater the possibility of random trivial breaks for
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to a threshold _θ_, where _θ > 2^-N_. The lower the value of _θ_ the greater the possibility of random trivial breaks for
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the party.
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* Parties who expect a large number of messages should choose to receive **all** messages for 2 reasons:
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1) Even high false positive rates for power users result in information leaks to the server (due to the large
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@ -1,7 +1,7 @@
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[package]
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name = "fuzzytags"
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description = "a probabilistic cryptographic structure for metadata resistant tagging"
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version = "0.1.2"
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version = "0.1.3"
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repository = "https://git.openprivacy.ca/openprivacy/fuzzytags"
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authors = ["Sarah Jamie Lewis <sarah@openprivacy.ca>"]
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edition = "2018"
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