Abstract
Mobile contact discovery is a convenience feature of messengers such as WhatsApp or Telegram that helps users to identify which of their existing contacts are registered with the service. Unfortunately, the contact discovery implementation of many popular messengers massively violates the users’ privacy as demonstrated by Hagen et al. (NDSS ’21, ACM TOPS ’23). Unbalanced private set intersection (PSI) protocols are a promising cryptographic solution to realize mobile private contact discovery, however, state-of-the-art protocols do not scale to real-world database sizes with billions of registered users in terms of communication and/or computation overhead.
In our work, we make significant steps towards truly practical large-scale mobile private contact discovery. For this, we combine and substantially optimize the unbalanced PSI protocol of Kales et al. (USENIX Security ’19) and the private information retrieval (PIR) protocol of Kogan and Corrigan-Gibbs (USENIX Security ’21). Our resulting protocol has a total communication overhead that is sublinear in the size of the server’s user database and also has sublinear online runtimes. We optimize our protocol by introducing database partitioning and efficient scheduling of user queries. To handle realistic change rates of databases and contact lists, we propose and evaluate different possibilities for efficient updates. We implement our protocol on smartphones and measure online runtimes of less than 2 s to query up to 1 024 contacts from a database with more than two billion entries. Furthermore, we achieve a reduction in setup communication up to factor \(32\times \) compared to state-of-the-art mobile private contact discovery protocols.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Albrecht, M.R., Rechberger, C., Schneider, T., Tiessen, T., Zohner, M.: Ciphers for MPC and FHE. In: EUROCRYPT (2015)
Ali, A., et al.: Communication-computation trade-offs in PIR. In: USENIX Security (2021)
Angel, S., Chen, H., Laine, K., Setty, S.T.V.: PIR with compressed queries and amortized query processing. In: S &P (2018)
Apple, Google: Exposure Notification Privacy-preserving Analytics (ENPA) White Paper (2021). https://covid19-static.cdn-apple.com/applications/covid19/current/static/contact-tracing/pdf/ENPA_White_Paper.pdf
Asharov, G., Lindell, Y., Schneider, T., Zohner, M.: More efficient oblivious transfer extensions with security for malicious adversaries. In: EUROCRYPT (2015)
Beimel, A., Ishai, Y., Malkin, T.: Reducing the servers computation in private information retrieval: PIR with preprocessing. In: CRYPTO (2000)
Bloom, B.H.: Space/Time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)
Boneh, D., Boyle, E., Corrigan-Gibbs, H., Gilboa, N., Ishai, Y.: Lightweight techniques for private heavy hitters. In: S &P (2021)
Borrello, P., Kogler, A., Schwarzl, M., Lipp, M., Gruss, D., Schwarz, M.: ÆPIC leak: Architecturally leaking uninitialized data from the microarchitecture. In: USENIX Security (2022)
Boyle, E., Gilboa, N., Ishai, Y.: Function secret sharing. In: EUROCRYPT (2015)
Boyle, E., Gilboa, N., Ishai, Y.: Function secret sharing: Improvements and extensions. In: CCS (2016)
Bui, D., Couteau, G.: Improved private set intersection for sets with small entries. In: PKC (2023)
Chen, H., Huang, Z., Laine, K., Rindal, P.: Labeled PSI from fully homomorphic encryption with malicious security. In: CCS (2018)
Chen, H., Laine, K., Rindal, P.: Fast private set intersection from homomorphic encryption. In: CCS (2017)
Cong, K., et al.: Labeled PSI from homomorphic encryption with reduced computation and communication. In: CCS (2021)
Corrigan-Gibbs, H., Boneh, D.: Prio: private, robust, and scalable computation of aggregate statistics. In: NSDI (2017)
Corrigan-Gibbs, H., Henzinger, A., Kogan, D.: Single-server private information retrieval with sublinear amortized time. In: EUROCRYPT (2022)
Corrigan-Gibbs, H., Kogan, D.: Private information retrieval with sublinear online time. In: EUROCRYPT (2020)
Cristofaro, E.D., Gasti, P., Tsudik, G.: Fast and private computation of cardinality of set intersection and union. In: CANS (2012)
Cristofaro, E.D., Tsudik, G.: Practical private set intersection protocols with linear complexity. In: FC (2010)
Cui, J., Yu, J.Z., Shinde, S., Saxena, P., Cai, Z.: SmashEx: smashing SGX enclaves using exceptions. In: CCS (2021)
Davidson, A., Pestana, G., Celi, S.: FrodoPIR: simple, scalable, single-server private information retrieval. PETS (2023)
Demmler, D., Rindal, P., Rosulek, M., Trieu, N.: PIR-PSI: scaling private contact discovery. PETS (2018)
Eppstein, D.: Cuckoo filter: simplification and analysis. In: SWAT (2016)
Facebook, Inc. (FB): First Quarter 2020 Results Conference Call (2020). https://s21.q4cdn.com/399680738/files/doc_financials/2020/q1/Q1’20-FB-Earnings-Call-Transcript.pdf
Fan, B., Andersen, D.G., Kaminsky, M., Mitzenmacher, M.: Cuckoo filter: practically better than bloom. In: CoNEXT (2014)
Freedman, M.J., Ishai, Y., Pinkas, B., Reingold, O.: Keyword search and oblivious pseudorandom functions. In: TCC (2005)
Garimella, G., Pinkas, B., Rosulek, M., Trieu, N., Yanai, A.: Oblivious key-value stores and amplification for private set intersection. In: CRYPTO (2021)
Ghosh, S.: Facebook probably has your phone number, even if you never shared it. Now it has a secret tool to let you delete it (2022). https://www.businessinsider.com/facebook-has-hidden-tool-to-delete-your-phone-number-email-2022-10
Gong, T., Henry, R., Psomas, A., Kate, A.: More is merrier in collusion mitigation (2022). CoRR ar**v:2305.08846
Günther, D., Heymann, M., Pinkas, B., Schneider, T.: GPU-accelerated PIR with client-independent preprocessing for large-scale applications. In: USENIX Security (2022)
Hagen, C., Weinert, C., Sendner, C., Dmitrienko, A., Schneider, T.: All the numbers are US: large-scale abuse of contact discovery in mobile messengers. In: NDSS (2021)
Hagen, C., Weinert, C., Sendner, C., Dmitrienko, A., Schneider, T.: Contact discovery in mobile messengers: Low-cost attacks, quantitative analyses, and efficient mitigations. TOPS (2023)
Hazay, C., Lindell, Y.: Efficient protocols for set intersection and pattern matching with security against malicious and covert adversaries. J. Cryptol. 23, 422–456 (2010)
Heinrich, A., Hollick, M., Schneider, T., Stute, M., Weinert, C.: PrivateDrop: Practical privacy-preserving authentication for Apple AirDrop. In: USENIX Security (2021)
Henry, R.: Polynomial batch codes for efficient IT-PIR. PETS (2016)
Henzinger, A., Hong, M.M., Corrigan-Gibbs, H., Meiklejohn, S., Vaikuntanathan, V.: One server for the price of two: Simple and fast single-server private information retrieval. In: USENIX Security (2023)
Hombashi, T.: Tcconfig (2022). https://github.com/thombashi/tcconfig
Internet Security Research Group: ISRG Prio Services for Preserving Privacy in COVID-19 EN Apps (2021). https://divviup.org/blog/prio-services-for-covid-en/
Internet Security Research Group: Divvi Up (2023). https://divviup.org/
Ion, M., et al.: On deploying secure computing: Private intersection-sum-with-cardinality. In: EuroS &P (2020)
Ishai, Y., Kushilevitz, E., Ostrovsky, R., Sahai, A.: Batch codes and their applications. In: STOC (2004)
Kales, D., Rechberger, C., Schneider, T., Senker, M., Weinert, C.: Mobile private contact discovery at scale. In: USENIX Security (2019)
Keller, M., Orsini, E., Scholl, P.: Actively secure OT extension with optimal overhead. In: CRYPTO (2015)
Kiss, Á., Liu, J., Schneider, T., Asokan, N., Pinkas, B.: Private set intersection for unequal set sizes with mobile applications. PETS (2017)
Kogan, D., Corrigan-Gibbs, H.: Private blocklist lookups with checklist. In: USENIX Security (2021)
Kolesnikov, V., Kumaresan, R., Rosulek, M., Trieu, N.: Efficient batched oblivious PRF with applications to private set intersection. In: CCS (2016)
Lazzaretti, A., Papamanthou, C.: Single server PIR with sublinear amortized time and polylogarithmic bandwidth. ePrint 2022/081 (2022)
Li, L., Pal, B., Ali, J., Sullivan, N., Chatterjee, R., Ristenpart, T.: Protocols for checking compromised credentials. In: SIGSAC (2019)
Liu, J., Li, J., Wu, D., Ren, K.: PIRANA: Faster multi-query PIR via constant-weight codes (2022). ePrint 2022/1401
Ma, Y., Zhong, K., Rabin, T., Angel, S.: Incremental Offline/Online PIR. In: USENIX Security (2022)
Meadows, C.A.: A more efficient cryptographic matchmaking protocol for use in the absence of a continuously available third party. In: S &P (1986)
Menon, S.J., Wu, D.J.: SPIRAL: fast, high-rate single-server PIR via FHE composition. In: S &P (2022)
Mughees, M.H., Chen, H., Ren, L.: OnionPIR: response efficient single-server PIR. In: CCS (2021)
Mughees, M.H., Ren, L.: Vectorized batch private information retrieval. S &P (2023)
Naor, M., Reingold, O.: Number-theoretic constructions of efficient pseudo-random functions. Journal of ACM 51(2), 231–262 (2004)
Nevo, O., Trieu, N., Yanai, A.: Simple, fast malicious multiparty private set intersection. In: CCS (2021)
Olson, P.: Facebook Closes \$19 Billion WhatsApp Deal (2014). https://www.forbes.com/sites/parmyolson/2014/10/06/facebook-closes-19-billion-whatsapp-deal/
Peikert, C., Vaikuntanathan, V., Waters, B.: A framework for efficient and composable oblivious transfer. In: CRYPTO (2008)
Pinkas, B., Schneider, T., Segev, G., Zohner, M.: Phasing: Private set intersection using permutation-based hashing. In: USENIX Security (2015)
Pinkas, B., Schneider, T., Smart, N.P., Williams, S.C.: Secure two-party computation is practical. In: AC (2009)
Pinkas, B., Schneider, T., Zohner, M.: Scalable private set intersection based on OT extension. TOPS (2018)
Raab, M., Steger, A.: “Balls into Bins” - A simple and tight analysis. In: RANDOM (1998)
Ragab, H., Milburn, A., Razavi, K., Bos, H., Giuffrida, C.: CrossTalk: Speculative data leaks across cores are real. In: S &P (2021)
Raghuraman, S., Rindal, P.: Blazing fast PSI from improved OKVS and subfield VOLE. In: CCS (2022)
Resende, A.C.D., Aranha, D.F.: Faster unbalanced private set intersection. In: FC (2018)
Rindal, P., Schoppmann, P.: VOLE-PSI: Fast OPRF and circuit-PSI from vector-OLE. In: EUROCRYPT (2021)
Shi, E., Aqeel, W., Chandrasekaran, B., Maggs, B.M.: Puncturable pseudorandom sets and private information retrieval with near-optimal online bandwidth and time. In: CRYPTO (2021)
Thomas, K., et al.: Protecting accounts from credential stuffing with password breach alerting. In: USENIX Security (2019)
Trieu, N., Shehata, K., Saxena, P., Shokri, R., Song, D.: Epione: lightweight contact tracing with strong privacy. IEEE Data Eng. Bull. 43(2), 95–107 (2020)
Troy Hunt: Have I Been Pwned: Check if your email has been compromised in a data breach (2023). https://haveibeenpwned.com/
van Schaik, S., Minkin, M., Kwong, A., Genkin, D., Yarom, Y.: CacheOut: leaking data on intel CPUs via cache evictions. In: S &P (2021)
Yao, A.C.C.: How to generate and exchange secrets (extended abstract). In: FOCS (1986)
Yeo, K.: Lower bounds for (batch) PIR with private preprocessing. In: EUROCRYPT (2023)
Zhou, M., Lin, W.K., Tselekounis, Y., Shi, E.: Optimal single-server private information retrieval. In: EUROCRYPT (2023)
Acknowledgements
This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 850990 PSOTI). It was co-funded by the Deutsche Forschungsgemeinschaft (DFG) within SFB 1119 CROSSING/236615297 and GRK 2050 Privacy & Trust/251805230.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hetz, L., Schneider, T., Weinert, C. (2024). Scaling Mobile Private Contact Discovery to Billions of Users. In: Tsudik, G., Conti, M., Liang, K., Smaragdakis, G. (eds) Computer Security – ESORICS 2023. ESORICS 2023. Lecture Notes in Computer Science, vol 14344. Springer, Cham. https://doi.org/10.1007/978-3-031-50594-2_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-50594-2_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50593-5
Online ISBN: 978-3-031-50594-2
eBook Packages: Computer ScienceComputer Science (R0)