Abstract
Fog computing enables low-latency services by positioning computational resources at the network edge but processing sensitive data on distributed and potentially untrusted nodes raises significant privacy concerns. While Trusted Execution Environments (TEEs) provide hardware-enforced security, they introduce substantial performance overhead (18-68ms cold start latency). Conversely, intelligent caching strategies optimize performance but assume trusted infrastructure, creating a fundamental gap between security and performance requirements. This paper presents a systematic investigation of privacy-preserving fog computing that integrates TEE-based security with intelligent caching. We synthesize recent advances across TEE privacy solutions, intelligent caching approaches, and integrated frameworks, analyzing trade-offs between security guarantees and performance metrics. Our analysis reveals that existing solutions address either security or performance in isolation, with limited work on joint optimization. We introduce POP2TIC, a four-tier framework combining hash-based caching (98% hit rate, 2128 keyspace) with optimized TEE pooling (8.5 ms warm start). Experimental evaluation demonstrates 3.16 ms average latency (75.3% reduction vs. existing fog solutions) and 91.7% attack detection rate while consuming 135% CPU and 299MB memory.