Optimized Security and Performance for Misinformation Detection in Distributed Caching Systems
Keywords:
Bloom filters, Cryptographic hashing, Distributed caching, Misinformation detection, Performance optimization, Reputation-based cachingAbstract
Distributed caching reduces content-delivery latency and network traffic, but cached objects can also accelerate the dissemination of manipulated or misleading information. This paper proposes a lightweight, multi-layer framework that combines probabilistic pre-screening, cryptographic integrity verification, and trust-aware cache retention. A Bloom filter is first used at the cache edge to rapidly identify objects associated with known misinformation indicators. Candidate objects then undergo SHA-256 verification against trusted reference hashes, separating fast screening from stronger integrity validation. Finally, a reputation-based policy assigns a time-to-live (TTL) value according to the reputation score of the content source, allowing trusted content to remain cached longer while reducing the persistence of content from less trusted sources. A simulation study uses 1,000 fixed-size digital objects with misinformation loads of 5%, 10%, and 15% and varies cache size, Bloom filter size, number of hash functions, and source reputation. Across the evaluated configurations, the analytical model reports detection accuracy from 99.672% to 99.949%, a modeled SHA-256 processing latency of 10.24 ms for a 1,024-byte object, and cache hit rates from 78% to 86%. The results indicate that combining complementary lightweight mechanisms can provide a practical security-performance trade-off for latency-sensitive distributed caching. The study also identifies real-time cache invalidation and adaptive reputation learning as important directions for future validation.
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