Stormy: Statistics in Tor by Measuring Securely

In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19).

Conference Version [pdf] Code Slides from CCS '19


Abstract: Tor is a tool for Internet privacy with millions of daily users. The Tor system benefits in many ways from information gathered about the operation of its network. Measurements guide operators in diagnosing problems, direct the efforts of developers, educate users about the level of privacy they obtain, and inform policymakers about Tor's impact. However, data collection and reporting can degrade user privacy, contradicting Tor's goals. Existing approaches to measuring Tor have limited capabilities and security weaknesses.
We present Stormy, a general-purpose, privacy-preserving measurement system that overcomes these limitations. Stormy uses secure multiparty computation (MPC) to compute any function of the observations made by Tor relays, while keeping those observations secret. Stormy makes use of existing efficient MPC protocols that are secure in the malicious model, and in addition it includes a novel input-sharing protocol that is secure, efficient, and fault tolerant. The protocol is non-interactive, which is consistent with how relays currently submit measurements, and it allows the relays to go offline after input submission, even while ensuring that an honest relay will not have its input excluded or modified. The input-sharing protocol is compatible with MPC protocols computing on authenticated values and may be of independent interest.
We show how Stormy can be deployed in two realistic models: (1) run primarily by a small set of dedicated authorities, or (2) run decentralized across the relays in the Tor network. Stormy scales efficiently to Tor's thousands of relays, tolerates network churn, and provides security depending only on either Tor's existing trust assumption that at least one authority is honest (in the first model) or the existing assumption that a large fraction of relay bandwidth is honest (in the second model).
We demonstrate how to use the system to compute two broadly-applicable statistics: the median of relay inputs and the cardinality of set-union across relays. We implement Stormy and experimentally evaluate system performance. When Stormy is run among authorities we can perform 151 median computations or 533 set-union cardinalities over 7,000 relay inputs in a single day. When run among the relays themselves, Stormy can perform 36 median computations or 134 set union cardinalities per day. Thus, both deployments enable non-trivial analytics to be securely computed in the Tor network.


            @inproceedings{wjsyg-stormy-ccs19,
             author = {Wails, Ryan and Johnson, Aaron and Starin, Daniel and Yerukhimovich, Arkady and Gordon, S. Dov},
             title = {Stormy: Statistics in Tor by Measuring Securely},
             booktitle = {Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security},
             series = {CCS '19},
             year = {2019},
             pages = {615--632},
             url = {http://doi.acm.org/10.1145/3319535.3345650},
             doi = {10.1145/3319535.3345650},
            }