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We study the problem of encrypting and authenticating quantum data in the presence of adversaries making adaptive chosen plaintext and chosen ciphertext queries. Classically, security games use string copying and comparison to detect adversarial cheating in such scenarios. Quantumly, this approach would violate no-cloning. We develop new techniques to overcome this problem: we use entanglement to detect cheating, and rely on recent results for characterizing quantum encryption schemes. We give definitions for (i) ciphertext unforgeability, (ii) indistinguishability under adaptive chosen-ciphertext attack, and (iii) authenticated encryption. The restriction of each definition to the classical setting is at least as strong as the corresponding classical notion: (i) implies INT-CTXT , (ii) implies IND-CCA2 , and (iii) implies AE . All of our new notions also imply QIND-CPA privacy. Combining one-time authentication and classical pseudorandomness, we construct symmetric-key quantum encryption schemes for each of these new security notions, and provide several separation examples. Along the way, we also give a new definition of one-time quantum authentication which, unlike all previous approaches, authenticates ciphertexts rather than plaintexts.
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This content will become publicly available on May 19, 2026
Rule-Based Hybrid Adaptive Encryption Model for Autonomous Flight to Secure UAS Data Streams in Real-Time
Given the increasing reliance on UAS in sensitive applications, ensuring the confidentiality, integrity, and availability of their data streams is paramount. Traditional encryption methods often fail to balance performance and security under real-time constraints. This paper addresses this gap by proposing a hybrid adaptive encryption framework that integrates rule-based (RL) logic and machine learning (ML) to dynamically adjust encryption protocols based on data sensitivity, bandwidth, and CPU load. The experimental results demonstrate improved responsiveness and security under varied conditions using real-time simulations. The effectiveness of the system is benchmarked through execution time analysis, classification accuracy, and adaptive decision precision, highlighting its potential for secure and efficient UAS communications.
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- Award ID(s):
- 2142514
- PAR ID:
- 10636646
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-9292-0
- Page Range / eLocation ID:
- 333 to 340
- Subject(s) / Keyword(s):
- adaptive encryption, data streams, cryptography, threat intelligence, autonomous systems, system resources, environmental factors
- Format(s):
- Medium: X
- Location:
- Tampa, FL, USA
- Sponsoring Org:
- National Science Foundation
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