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We present and analyze UDM, a new protocol for user discovery in anonymous communication systems that minimizes the information disclosed to the system and users. Unlike existing systems, including those based on private set intersection, UDM learns nothing about the contact lists and social graphs of the users, is not vulnerable to off-line dictionary attacks that expose contact lists, does not reveal platform identifiers to users without the owner’s explicit permission, and enjoys low computation and communication complexity. UDM solves the following user-discovery problem. User Alice wishes to communicate with Bob over an anonymous communication system, such as cMix or Tor. Initially, each party knows each other’s public contact identifier (e.g., email address or phone number), but neither knows the other’s private platform identifier in the communication system. If both parties wish to communicate with each other, UDM enables them to establish a shared key and learn each other’s private platform identifier. UDM uses an untrusted user-discovery system, which processes and stores only public information, hashed values, or values encrypted with keys it does not know. Therefore, UDM cannot learn any information about the social graphs of its users. Using the anonymous communication system, each pair of users who wish to communicate with each other uploads to the user-discovery system their private platform identifier, encrypted with their shared key. Indexing their request by a truncated cryptographic hash of their shared key, each user can then download each other’s encrypted private platform identifier.
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Mycelium: Large-Scale Distributed Graph Queries with Differential Privacy
This paper introduces Mycelium, the first system to process differentially private queries over large graphs that are distributed across millions of user devices. Such graphs occur, for instance, when tracking the spread of diseases or malware. Today, the only practical way to query such graphs is to upload them to a central aggregator, which requires a great deal of trust from users and rules out certain types of studies entirely. With Mycelium, users' private data never leaves their personal devices unencrypted, and each user receives strong privacy guarantees. Mycelium does require the help of a central aggregator with access to a data center, but the aggregator merely facilitates the computation by providing bandwidth and computation power; it never learns the topology of the graph or the underlying data. Mycelium accomplishes this with a combination of homomorphic encryption, a verifiable secret redistribution scheme, and a mix network based on telescoping circuits. Our evaluation shows that Mycelium can answer a range of different questions from the medical literature with millions of devices.
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- PAR ID:
- 10312007
- Date Published:
- Journal Name:
- ACM SIGOPS Symposium on Operating Systems Principles (SOSP)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3477132.3483585
