Abstract
The interplay between cryptography and access control has been widely investigated in the literature. For example, attribute-based encryption (ABE) is a leading candidate of a cryptographic tool going beyond the all-or-nothing approach of public-key encryption by supporting fine-grained access control for encrypted data. Unfortunately, the deployment and adoption of ABE have been slow, and (to the best of our knowledge) few commercial widely-used products use it to date. In particular, selective and fine-grained control over what is shared, and with whom, is absent from common data products and formats, such as those generated by commercial authoring products, e.g., Microsoft Word documents, Excel spreadsheets, PowerPoint slides. This lack of selective and fine-grained control results in users simply not sharing. This major usability shortcoming impacts defense and military coalition operations, as well as commercial settings, such as life sciences, healthcare, and the financial sectors.
This paper addresses the above usability problem head-on by proposing a crypto- graphically enforced selective access control in Microsoft Office products and similar platforms. We focus on Excel as an illustrative use-case, but note that our work is applicable to (and is already implemented for) other Microsoft products such as Word, PowerPoint, and Outlook. Using the JavaScript API for Microsoft Office, we designed and developed simple add-ins that enable cell encryption according to a policy, and requires a key that embeds attributes satisfying the policy in order to decrypt. Our performance evaluation not only shows that cryptographic-based selective sharing of information in widely-deployed and widely-used commercial authoring and collaboration platforms is possible, but also practical.
K. Eldefrawy and T. Lepoint—Contact authors.
T. Lepoint—Work performed while at SRI International.
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Notes
- 1.
We extended the work while under review to enable row, and/or column, or full document encryption.
- 2.
Note that in our add-in, we load the formulas of the cells, and not the displayed text values (Fig. 1). This enables to recover cell inputs, such as "=SUM(A1:A10)", that compute over cell ranges, and hence to keep the dynamicity of the spreadsheet.
- 3.
Note that this policy makes sense; e.g., Russia or Turkey could be potential intended recipients of such a policy.
- 4.
More precisely, it allows the creation of conjunctive normal forms (CNF).
- 5.
- 6.
- 7.
Obviously, the longer the text in the cells, the larger the documents will be. We use the default secret-key authenticated encryption of TweetNaCl.js (XSalsa20-Poly1305); hence the size of each ciphertext is 16 bytes longer than the original message.
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References
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National Defense Authorization Act for the fiscal year 2000. https://www.congress.gov/106/plaws/publ65/PLAW-106publ65.pdf
Office add-ins platform overview. https://docs.microsoft.com/en-us/office/dev/add-ins/overview/office-add-ins
PBC library. https://crypto.stanford.edu/pbc/
scrypt-async. https://github.com/dchest/scrypt-async-js
TweetNaCl.js. https://tweetnacl.js.org/
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Acknowledgments
The authors thank Tim Ellis, Ron Moore, and Karen Myers for helpful discussions and suggestions. This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) and Space and Naval Warfare Systems Center, Pacific (SSC Pacific) under Contract No. N66001-15-C-4071. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA or SSC Pacific. This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA). The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.
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Eldefrawy, K., Lepoint, T., Tam, L. (2022). In-App Cryptographically-Enforced Selective Access Control for Microsoft Office and Similar Platforms. In: Dolev, S., Katz, J., Meisels, A. (eds) Cyber Security, Cryptology, and Machine Learning. CSCML 2022. Lecture Notes in Computer Science, vol 13301. Springer, Cham. https://doi.org/10.1007/978-3-031-07689-3_32
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