mlsanityData to 14 Oct 2026

SHARD: cell-keyed residual splitting for alignment-resistant private dense retrieval

arXiv 2606.27976 ↗First posted 26 Jun 2026cs.CR cs.AI cs.IRcited by 0

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Abstract (arXiv)

Dense retrieval systems expose document geometry when vector stores are compromised, and a global protective transform can often be aligned from known pairs. We study SHARD, which splits PCA coordinates into a short routing prefix and a residual protected by independent cell-local orthogonal keys. It supports CKKS ciphertext--plaintext reranking but is evaluated as a leakage trade-off, not a cryptographic document-privacy guarantee. Corrected scoring uses centered document coordinates and an uncentered scoring query, preserving raw ranking up to a query-dependent constant. Across ten BEIR/MIRACL configurations it reproduces raw nDCG@10 and recall, whereas centering both sides loses up to 0.080 nDCG. Cell keys spread diffuse known-pair evidence across compartments, but minimum-norm alignment recovers useful signal far below full key rank, so there is no hard de-anonymization threshold. Real CKKS has maximum score error 2.29e-6 and no top-1 flips; block packing cuts query upload by 74--87% but raises in-process p50 latency by 14--26%. In a strengthened GTR case, an unknown key lowers token-F1 from 0.665 to 0.242; a wide prefix and eight pairs restore much. Under 25--90% release overlap, the unchanged prefix and clean residual norm link persistent rows with R@1 at least 0.9996, although cell-Gram linkage degrades under churn. A formally calibrated Gaussian release gives nDCG@10 at most 0.011 at epsilon=1; its only three strict utility matches occur at epsilon=32768 with linkage R@1 at least 0.995. SHARD preserves retrieval and compartmentalizes alignment evidence, but does not provide DP, unlinkability, or cancellable templates.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 61 references that are in the map. First posted on arXiv 26 Jun 2026.

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32 of 61 references.

References · 32

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20 more
  1. 2018Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoisingcited by 218
  2. 2020Information Leakage in Embedding Modelscited by 203
  3. 2023Text Embeddings Reveal (Almost) As Much As Textcited by 192
  4. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
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  11. 2021Intel HEXL: Accelerating Homomorphic Encryption with Intel AVX512-IFMA52cited by 8
  12. 2024Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimizationcited by 8
  13. 2025Universal Zero-shot Embedding Inversioncited by 8
  14. 2025Rethinking the Privacy of Text Embeddings: A Reproducibility Study of "Text Embeddings Reveal (Almost) As Much As Text"cited by 6
  15. 2025Privacy-Preserving LLM Embedding Transmission for End-Cloud Collaborationcited by 5
  16. 2025Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignmentcited by 3
  17. 2026Concept-Aware Privacy Mechanisms for Defending Embedding Inversion Attackscited by 2
  18. 2025LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimizationcited by 1
  19. 2026Nonparametric Variational Differential Privacy via Embedding Parameter Clippingcited by 1
  20. 2026Hybrid privacy-aware semantic search: SVD-truncated document geometry and CKKS-encrypted query reranking under a restricted threat modelcited by 1

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