Abstract
Emails have improved our workplace efficiency and communication. However, they are often processed unencrypted by mail servers, leaving them open to data breaches on a single service provider. Public-key based solutions for end-to-end secured email, such as Pretty Good Privacy (PGP) and Secure/Multipurpose Internet Mail Extensions (S/MIME), are available but are not widely adopted due to usability obstacles and also hinder processing of encrypted emails.
We propose PrivMail, a novel approach to secure emails using secret sharing methods. Our framework utilizes Secure Multi-Party Computation techniques to relay emails through multiple service providers, thereby preventing any of them from accessing the content in plaintext. Additionally, PrivMail supports private server-side email processing similar to IMAP SEARCH, and eliminates the need for cryptographic certificates, resulting in better usability than public-key based solutions. An important aspect of our framework is its capability to enable third-party searches on user emails while maintaining the privacy of both the email and the query used to conduct the search.
To evaluate our solution, we benchmarked transfer and search operations using the Enron Email Dataset and demonstrate that PrivMail is an effective solution for enhancing email security.
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Notes
- 1.
Mostly the subject and content fields but not other meta data.
- 2.
- 3.
The older Post Office Protocol (POP) downloads the email from the server and optionally deletes it from the server, but in contrast to IMAP provides no server-side search.
- 4.
Later in Sect. 3.2 we describe an optimization to send a seed for a Pseudo Random Function (PRF) instead of the whole share \(\textsf{E}_{1}\).
- 5.
The agent \(\mathcal {A}\) generates the shares of the mask and sends them with the keyword shares.
- 6.
References
Docker Container. https://www.docker.com
imaplib. https://docs.python.org/3/library/imaplib.html#imaplib.IMAP4.fetch
YAML Data Serialization Language. https://yaml.org
Apple and Google: Exposure Notification Privacy-Preserving Analytics (ENPA) white paper (2021)
Atkins, D., Stallings, W., Zimmermann, P.: PGP Message Exchange Formats. RFC 1991 (1996). https://www.rfc-editor.org/rfc/rfc1991.txt
Baron, J., Defrawy, K.E., Minkovich, K., Ostrovsky, R., Tressler, E.: 5PM: secure pattern matching. In: SCN (2012)
Ben-Or, M., Goldwasser, S., Wigderson, A.: Completeness theorems for non-cryptographic fault-tolerant distributed computation (extended abstract). In: STOC (1988)
Blog, M.S.: Next steps in privacy-preserving Telemetry with Prio (2019). https://blog.mozilla.org/security/2019/06/06/next-steps-in-privacy-preserving-telemetry-with-prio/
Braun, L., Demmler, D., Schneider, T., Tkachenko, O.: MOTION - a framework for mixed-protocol multi-party computation. ACM TOPS 25(2), 1–35 (2021)
Chandran, G.R., Nieminen, R., Schneider, T., Suresh, A.: PrivMail: a privacy-preserving framework for secure emails (full version). ePrint Archive, Paper 2023/1294 (2023). https://encrypto.de/code/PrivMail
Chase, M., Shen, E.: Substring-searchable symmetric encryption. PoPETs (2015)
Chor, B., Goldreich, O., Kushilevitz, E., Sudan, M.: Private information retrieval. In: FOCS (1995)
Crispin, M.: Internet Message Access Protocol - Version 4rev1. RFC 3501 (2003). https://rfc-editor.org/rfc/rfc3501.txt
Demmler, D., Schneider, T., Zohner, M.: ABY – a framework for efficient mixed-protocol secure two-party computation. In: NDSS (2015)
Demmler, D., Herzberg, A., Schneider, T.: RAID-PIR: practical multi-server PIR. In: CCSW (2014)
Demmler, D., Holz, M., Schneider, T.: OnionPIR: effective protection of sensitive metadata in online communication networks. In: Gollmann, D., Miyaji, A., Kikuchi, H. (eds.) ACNS 2017. LNCS, vol. 10355, pp. 599–619. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61204-1_30
ETail Emarsys WBR SMB Report: Adapting to the pace of omnichannel commerce (2016). https://emarsys.com/learn/white-papers/adapting-to-the-pace-of-omnichannel-commerce/
Fireblocks: MPC Wallet as a Service Technology (2022). https://www.fireblocks.com/platforms/mpc-wallet/
Franceschi-Bicchierai, L.: T-Mobile says hacker accessed personal data of 37 million customers (2023). https://techcrunch.com/2023/01/19/t-mobile-data-breach/
Gennaro, R., Hazay, C., Sorensen, J.S.: Text search protocols with simulation based security. In: Nguyen, P.Q., Pointcheval, D. (eds.) PKC 2010. LNCS, vol. 6056, pp. 332–350. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13013-7_20
Gilbert, N.: Number of Email Users Worldwide 2022/2023: Demographics & Predictions (2022). https://financesonline.com/number-of-email-users/
Goldreich, O., Micali, S., Wigderson, A.: How to play any mental game: a completeness theorem for protocols with honest majority. In: STOC (1987)
The Radicati Group, Inc.: Email Statistics Report, 2019–2023 (2018). https://www.radicati.com/wp/wp-content/uploads/2018/12/Email-Statistics-Report-2019-2023-Executive-Summary.pdf
Gui, Z., Paterson, K.G., Patranabis, S.: Rethinking searchable symmetric encryption. In: S &P (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). https://doi.org/10.1007/s00145-008-9034-x
Hazay, C., Toft, T.: Computationally secure pattern matching in the presence of malicious adversaries. J. Cryptol. 27, 358–395 (2014). https://doi.org/10.1007/s00145-013-9147-8
Huang, Y., Evans, D., Katz, J.: Private set intersection: are garbled circuits better than custom protocols? In: NDSS (2012)
IBM Security: Cost of a Data Breach Report 2023 (2023). https://www.ibm.com/reports/data-breach
Inpher: XOR Secret Computing Engine (2022). https://inpher.io/xor-secret-computing/
Jha, S., Kruger, L., Shmatikov, V.: Towards practical privacy for genomic computation. In: S &P(2008)
Kamara, S., Kati, A., Moataz, T., Schneider, T., Treiber, A., Yonli, M.: SoK: cryptanalysis of encrypted search with LEAKER - a framework for LEakage AttacK Evaluation on Real-world data. In: EuroS &P (2022)
Katz, J., Malka, L.: Secure text processing with applications to private DNA matching. In: CCS(2010)
Klensin, D.J.C.: Simple Mail Transfer Protocol. RFC 5321 (2008). https://rfc-editor.org/rfc/rfc5321.txt
Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22. https://www.cs.cmu.edu/~./enron/
Kolesnikov, V., Sadeghi, A.-R., Schneider, T.: Improved garbled circuit building blocks and applications to auctions and computing minima. In: Garay, J.A., Miyaji, A., Otsuka, A. (eds.) CANS 2009. LNCS, vol. 5888, pp. 1–20. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-10433-6_1
Kolesnikov, V., Schneider, T.: Improved garbled circuit: free XOR gates and applications. In: Aceto, L., Damgård, I., Goldberg, L.A., Halldórsson, M.M., Ingólfsdóttir, A., Walukiewicz, I. (eds.) ICALP 2008. LNCS, vol. 5126, pp. 486–498. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70583-3_40
Koti, N., Patra, A., Rachuri, R., Suresh, A.: Tetrad: actively secure 4PC for secure training and inference. In: NDSS (2022)
Martinoli, M.: Behind the scenes of ProtonMail’s message content search (2022). https://proton.me/blog/engineering-message-content-search
Mohassel, P., Rindal, P., Rosulek, M.: Fast database joins and PSI for secret shared data. In: CCS (2020)
Namjoshi, K.S., Narlikar, G.J.: Robust and fast pattern matching for intrusion detection. In: INFOCOM (2010)
Osadchy, M., Pinkas, B., Jarrous, A., Moskovich, B.: SCiFI - a system for secure face identification. In: S &P(2010)
Oya, S., Kerschbaum, F.: Hiding the access pattern is not enough: exploiting search pattern leakage in searchable encryption. In: USENIX Security (2021)
Page, C., Whittaker, Z.: It’s All in the (Lack of) Details: 2022’s badly handled data breaches (2022). https://techcrunch.com/2022/12/27/badly-handled-data-breaches-2022/
Patra, A., Schneider, T., Suresh, A., Yalame, H.: ABY2.0: improved mixed-protocol secure two-party computation. In: USENIX Security (2021)
Perlroth, N.: Yahoo Says Hackers Stole Data on 500 Million Users in 2014 (2016). https://www.nytimes.com/2016/09/23/technology/yahoo-hackers.html
Pinkas, B., Schneider, T., Tkachenko, O., Yanai, A.: Efficient circuit-based PSI with linear communication. In: Ishai, Y., Rijmen, V. (eds.) EUROCRYPT 2019. LNCS, vol. 11478, pp. 122–153. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17659-4_5
Raymond, E.S.: AIS Payload Data Types (2017). https://gpsd.gitlab.io/gpsd/AIVDM.html
Ruoti, S., et al.: A usability study of four secure email tools using paired participants. ACM TOPS 22(2), 1–33 (2019)
Ruoti, S., Andersen, J., Zappala, D., Seamons, K.E.: Why Johnny still, still can’t encrypt: evaluating the usability of a modern PGP client. CoRR 1510.08555 (2015)
Schaad, J., Ramsdell, B.C., Turner, S.: Secure/Multipurpose Internet Mail Extensions (S/MIME) Version 4.0 Message Specification. RFC 8551 (2019). https://rfc-editor.org/rfc/rfc8551.txt
Schneider, T., Zohner, M.: GMW vs. Yao? Efficient secure two-party computation with low depth circuits. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 275–292. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39884-1_23
Sepior: Advanced MPC Wallet™ (2022). https://sepior.com/products/advanced-mpc-wallet/
Simmons, D.: 17 Countries with GDPR-Like Data Privacy Laws (2022). https://insights.comforte.com/countries-with-gdpr-like-data-privacy-laws
Song, D.X., Wagner, D.A., Perrig, A.: Practical techniques for searches on encrypted data. In: S &P (2000)
Song, V.: Mother of All Breaches Exposes 773 Million Emails, 21 Million Passwords (2019). https://gizmodo.com/mother-of-all-breaches-exposes-773-million-emails-21-m-1831833456
Proton Technologies: ProtonMail Security Features and Infrastructure (2016). https://protonmail.com/docs/business-whitepaper.pdf
Troncoso-Pastoriza, J.R., Katzenbeisser, S., Celik, M.U.: Privacy preserving error resilient DNA searching through oblivious automata. In: CCS (2007)
Tutanota: Secure email made for you. https://tutanota.com/security
Tutanota: Searching encrypted data is now possible with Tutanota’s innovative feature (2017). https://tutanota.com/blog/posts/first-search-encrypted-data
Watson, T.: The number of email addresses people use [survey data] (2019). https://www.zettasphere.com/how-many-email-addresses-people-typically-use
Wei, X., Zhao, M., Xu, Q.: Efficient and secure outsourced approximate pattern matching protocol. Soft. Comput. 22, 1175–1187 (2018). https://doi.org/10.1007/s00500-017-2560-4
Yasuda, M., Shimoyama, T., Kogure, J., Yokoyama, K., Koshiba, T.: Privacy-preserving wildcards pattern matching using symmetric somewhat homomorphic encryption. In: Susilo, W., Mu, Y. (eds.) ACISP 2014. LNCS, vol. 8544, pp. 338–353. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08344-5_22
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.
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A Comparison of the Different Search Techniques
A Comparison of the Different Search Techniques
Each of the search techniques discussed in Sect. 4, i.e., circuit-based, bucketing-based, and indexing-based, have their own pros and cons depending on the keywords being searched. The user or the email client can therefore choose the most beneficial technique according to their requirements. In Table 2, we give a comparison between the different techniques for various use-cases, highlighting the most efficient techniques for each use-case.
Performance of Circuit-Based Search. Table 3 summarizes our benchmarks for search across four different keyword lengths, \(s\in \{3,8,13,18\}\) (corresponding to the average of our bucket sizes), on email sets of sizes 100 and 200. The total computation and communication overheads grow proportionally to the keyword length and number of emails in the sets as the search circuit size grows.
We parallelize each equality test circuit (see Eq. (1)) with Single Instruction, Multiple Data (SIMD) operations, which results in an almost linear total runtime with respect to the keyword length. The minor difference in online runtime is caused by runtime fluctuations in our WAN simulation and can be evened out with additional iterations. The remaining cumulative OR in Eq. (2) dominates the online runtime, giving a nearly constant runtime.
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Chandran, G.R., Nieminen, R., Schneider, T., Suresh, A. (2024). PrivMail: A Privacy-Preserving Framework for Secure Emails. In: Tsudik, G., Conti, M., Liang, K., Smaragdakis, G. (eds) Computer Security – ESORICS 2023. ESORICS 2023. Lecture Notes in Computer Science, vol 14345. Springer, Cham. https://doi.org/10.1007/978-3-031-51476-0_8
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