Dense embeddings underpin semantic search and retrieval-augmented generation, yet a leaked vector store hands much of the underlying text back. Modern inversion and alignment attacks share one weakness: the protected store is a single global geometry, and any single geometry can be aligned to a known one - a secret global rotation included, since orthogonal Procrustes recovers it from about subspace-dimension known-plaintext pairs. We introduce SHARD, a retrieval-preserving embedding transform that removes that weak axis. The centred embedding is rotated and split into a short public prefix (driving stage-1 retrieval) and a private residual sharded into C cells, each rotated under a separate secret key; the residual is reranked under CKKS, where the keys cancel and the inner product stays exact. One parameter C spans the global-linear baseline (C=1) to per-document micro-keys (C=N), making the keyed residual a cancellable template - revocable, renewable, unlinkable - for text embeddings, the first such scheme for dense retrieval. On five encoders: full-dimensional reranking returns the raw-space nDCG@10 that half-SVD truncation gives up; recovering the cell-keyed residual under a diffuse known-plaintext leak costs about C times more anchors (median 200 to 102,400 at C=256) for a few encrypted residual queries and the short public prefix leaks far less neighbour structure, with a micro-key limit driving residual leakage to zero. The barrier holds against learned-linear, non-linear and unsupervised aligners, and where a matched-utility noise defence de-anonymises almost every probe, SHARD de-anonymises none. Limits: within a cell similarities survive, a targeted attacker on one victim's cell needs only about d_priv anchors, and an overlapping reference corpus still leaks through the public prefix. SHARD is an attack-aware geometric defence, not a cryptographic guarantee.

翻译：暂无翻译