This article presents an extended theoretical synthesis and a comprehensive conceptual framework for designing resilient, privacy-preserving, and QoS-aware multi-tenant cloud systems by integrating four complementary technological and architectural paradigms: blockchain-based decentralized control and provenance, trusted execution environments (TEEs) exemplified by Intel SGX and SGX-aware container runtimes, formalized privacy mechanisms grounded in differential privacy and randomized response, and adaptive tenant separation and detection strategies for runtime isolation and attack mitigation. We examine the strengths, limitations, and interplay among these approaches, and propose a unified architecture that reconciles competing objectives: strong confidentiality and integrity guarantees for tenant data, practical auditability and accountability in federated or multi-cloud deployments, minimal performance degradation under realistic service level agreements, and robust detection and mitigation of VM- and container-based threats including botclouds and distributed denial of service (DDoS). Building on foundational literature in cloud security, privacy, and multi-tenant orchestration, we elaborate a layered methodology that combines (a) blockchain-anchored metadata and access-control contracts for decentralized provenance and SLA enforcement, (b) enclave-protected computation and SCONE-like secure container frameworks for limiting the trusted computing base, (c) differential privacy mechanisms and RAPPOR-style telemetry sanitization to constrain information leakage from aggregated metrics, and (d) fine-grained, SLA-aware tenant separation with multi-level authorization and reputation mechanisms to reduce lateral movement and noisy neighbor effects. We discuss expected tradeoffs, emergent attack surfaces introduced by combined deployments, and measurable indicators for security, privacy, and QoS that operational teams can use for continuous assurance. Finally, the paper outlines open research directions, including verification of blockchain smart contracts for SLA semantics, long-term key management for TEEs in federated clouds, rigorous composition theorems for differential privacy under repeated queries in multi-tenant analytics, and adaptive controllers for load distribution that account for anonymity-preserving telemetry. The synthesis aims to serve as a rigorous theoretical scaffold for experimental systems research and industrial adoption, enabling future empirical evaluation and standardization.