Distributed vector databases increasingly support security-sensitive applications, yet existing systems primarily protect index integrity or replica availability rather than the trustworthiness of retrieved results. Consequently, a compromised replica can return a plausible but manipulated result that remains cryptographically valid, while conventional replication and Byzantine defenses do not directly establish whether the retrieved results are trustworthy. We present TrustANN, a middleware layer that provides query-level trust for distributed approximate nearest-neighbor (ANN) retrieval without modifying the underlying ANN algorithm. TrustANN introduces three complementary protocols: TrustBind, which verifies retrieval trust from authenticated, consistent, and failure-domain-independent replica evidence; CoordShift, which asynchronously constructs reusable coordination epochs outside the query critical path; and AvailGuard, which performs bounded recovery when replicas are unavailable, stale, or conflicting while preserving the trust criterion. Queries are marked Trusted only when sufficient independent evidence exists for every relevant shard; otherwise, TrustANN returns Unverified. On an AWS deployment using distributed FAISS HNSW, TrustANN reduces retrieval attack success rate from 1.000 to 0.000 under the tested Byzantine-retrieval attack with two-of-three replica verification. The verification layer increases clean p95 latency by only 9.36%. Under replica crashes, TrustANN maintains trusted retrieval through 33% failures and correctly returns Unverified when sufficient evidence is unavailable.
Distributed vector stores increasingly serve multimodal workloads whose representations are jointly accessed but independently partitioned and placed. As a result, cross-modal physical locality remains largely unoptimized. Multimodal queries can incur remote accesses and cross-tier data movement even when each modality's ANN search remains efficient. We present Coloc, a storage-management layer that makes cross-modal access locality a first-class data-layout signal. A Cross-Modal Access Graph combines semantic affinity, co-access, and temporal behavior. A movement-aware optimizer places frequently co-accessed vectors under capacity and topology constraints. A migration manager adapts placement as workloads shift by weighing locality gains against migration cost. A confidence-gated prefetcher hides remaining slow-tier latency. Coloc operates above existing ANN indexes without modifying embeddings, search, or retrieval semantics. We deploy Coloc on a multi-tier AWS testbed spanning GPU, NUMA-DRAM, NVMe, and S3. Coloc substantially reduces data movement and query latency while leaving ANN retrieval unchanged. Under a scheduled workload shift, its migration manager matches the locality of periodic global reorganization while moving 5.9x less data and completing 3.9x faster.