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----
-title: "Private Contact Discovery"
-date: 2019-06-22T10:30:00+08:00
----
-
-Private Contact Discovery
-=========================
-
-Private Contact Discovery (PCD) is the formal name for the problem modern smartphone messenger applications have on
-installation: Given a user's address book, find out which of their contacts also use the same messenger without the
-messenger's servers learning anything about the user's address book. The widespread non-private way to do this is to
-simply upload the user's address book to the app's operator's servers and do an SQL JOIN keyed on the phone number field
-against the database of registered users. People have tried sprinkling some hashes over these phone numbers in an
-attempt to improve privacy, but obviously running a brute-force preimage attack given a domain of maybe a few billion
-valid inputs is not cryptographically hard.
-
-Private Contact Discovery can be phrased in terms of Private Set Intersection (PSI), the cryptographic problem of having
-two parties holding one set each find the intersection of their sets without disclosing any other information. PSI has
-been an active field of research for a while and already yielded useful results for some use cases. Alas, none of those
-results were truly practical yet for usage in PCD in a typical messenger application. They would require too much CPU
-time or too much data to be transferred.
-
-At USENIX Security 2019, Researchers from technical universities Graz and Darmstadt published a paper titled *Private
-Contact Discovery at Scale*
-(`eprint <https://eprint.iacr.org/2019/517>`__ | `PDF <https://eprint.iacr.org/2019/517.pdf>`__).
-In this paper, they basically optimize the hell out of existing cryptographic solutions to private contact discovery,
-jumping from a still-impractical state of the art right to practicality. Their scheme allows a client with 1k contacts
-to run PCD against a server with 1B contacts in about 3s on a phone. The main disadvantage of their scheme is that it
-requires the client to in advance download a compressed database of all users, that clocks in at about 1GB for 1B users.
-
-I found this paper very interesting for its immediate practical applicability. As an excuse to dig into the topic some
-more, I gave a short presentation at my university lab's research seminar on this paper
-(slides: `PDF <mori_semi_psi_talk.pdf>`__ | `ODP <mori_semi_psi_talk.odp>`__).
-
-Even if you're not working on secure communication systems on a day-to-day basis this paper might interest you. If
-you're working with social account information of any kind I can highly recommend giving it a look. Not only might your
-users benefit from improved privacy, but your company might be able to avoid a bunch of data protection and
-accountability issues by simply not producing as much sensitive data in the first place.
diff --git a/content/posts/private-contact-discovery/mori_semi_psi_talk.odp b/content/posts/private-contact-discovery/mori_semi_psi_talk.odp
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