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FABESA: Fast (and Anonymous) Attribute-Based Encryption under Standard Assumption

Authors: Long Meng; Liqun Chen 0002; Yangguang Tian; Mark Manulis;

FABESA: Fast (and Anonymous) Attribute-Based Encryption under Standard Assumption

Abstract

DOI10.1145/3658644.3670321 ABSTRACT Attribute-Based Encryption (ABE) provides fine-grained access control to encrypted data and finds applications in various domains. The practicality of ABE schemes hinges on the balance between security and efficiency. The state-of-the-art adaptive secure ABE scheme, proven to be adaptively secure under standard assumptions (FAME, CCS'17), is less efficient compared to the fastest one (FABEO, CCS'22) which is only proven secure under the Generic Group Model (GGM). These traditional ABE schemes focus solely on message privacy. To address scenarios where attribute value information is also sensitive, Anonymous ABE (A2BE) ensures the privacy of both the message and attributes. However, most A2BE schemes suffer from intricate designs with low efficiency, and the security of the fastest key-policy A2BE (proposed in FEASE, USENIX'24) relies on the GGM. In this paper, we propose novel fast key-policy and ciphertext-policy ABE schemes that (1) support both AND and OR gates for access policies, (2) have no restriction on the size and type of policies or attributes, (3) achieve adaptive security under the standard DLIN assumption, and (4) only need 4 pairings for decryption. As our ABE constructions automatically provide ciphertext anonymity, we easily transform our ABE schemes to A2BE schemes while maintaining the same features and high-level efficiency. The implementation results show that all our schemes achieve the best efficiency comparing to other schemes with adaptive security proven under standard assumptions. Specifically, our ABE schemes perform better than FAME and are close to FABEO. Our key-policy A2BE scheme performs close to the one in FEASE and our ciphertext-policy A2BE outperforms the state-of-the-art (Cui et al., ProvSec'16). AUTHORS Long Meng, University of Surrey Liqun Chen, University of Surrey Yangguang Tian, University of Surrey Mark Manulis, Universität der Bundeswehr München

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
6
Top 10%
Average
Top 10%