Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases | Paper Citation Record

Observation d39d57ab-dcee-47e0-9b96-2dd17cd14a12 · outbound

This paper cites Regulation (eu) 2016/679 of the european parliament and of the council (general data protection regulation),.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Regulation (eu) 2016/679 of the european parliament and of the council (general data protection regulation),

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This paper cites Guidelines 05/2019 on the right to erasure,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Guidelines 05/2019 on the right to erasure,

Reference 2

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This paper cites Humans forget, machines remember: Artificial intelligence and the right to be forgotten,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Humans forget, machines remember: Artificial intelligence and the right to be forgotten,

Reference 3

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This paper cites Forgetting personal data and revoking consent under the gdpr: Challenges and proposed solutions,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Forgetting personal data and revoking consent under the gdpr: Challenges and proposed solutions,

Reference 4

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This paper cites Negligible Effects of Baryons on the Angular Momentum Scaling Relations of Galactic Dark Matter Halos.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Negligible Effects of Baryons on the Angular Momentum Scaling Relations of Galactic Dark Matter Halos

Reference 5

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This paper cites Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs

Reference 6

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This paper cites Efficient indexing of billion-scale datasets of deep descriptors,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Efficient indexing of billion-scale datasets of deep descriptors,

Reference 7

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This paper cites Chromadb,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Chromadb,

Reference 8

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This paper cites Billion-scale similarity search with gpus,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Billion-scale similarity search with gpus,

Reference 9

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This paper cites The Faiss library.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases The Faiss library

Reference 10

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This paper cites Weaviate,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Weaviate,

Reference 11

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This paper cites Pinecone vector database,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Pinecone vector database,

Reference 12

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This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 13

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This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Sentence-bert: Sentence embeddings using siamese bert-networks,

Reference 14

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This paper cites A deep learning-driven framework for detecting anomalous data breaches in distributed cloud storage infrastructures,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases A deep learning-driven framework for detecting anomalous data breaches in distributed cloud storage infrastructures,

Reference 15

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This paper cites Guidance regarding methods for de-identification of protected health information in accordance with the HIPAA privacy rule,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Guidance regarding methods for de-identification of protected health information in accordance with the HIPAA privacy rule,

Reference 16

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This paper cites Relations among notions of security for public-key encryption schemes,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Relations among notions of security for public-key encryption schemes,

Reference 17

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This paper cites Fips pub 197: Advanced encryption standard (aes),.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Fips pub 197: Advanced encryption standard (aes),

Reference 18

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This paper cites Authenticated encryption in the public-key setting: Security notions and analyses,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Authenticated encryption in the public-key setting: Security notions and analyses,

Reference 19

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This paper cites Generating classes of test data using fake data generation and static data manipulation,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Generating classes of test data using fake data generation and static data manipulation,

Reference 20

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This paper cites Mimic-iii, a freely accessible critical care database,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Mimic-iii, a freely accessible critical care database,

Reference 21

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This paper cites Labeled faces in the wild: A database for studying face recognition in uncon- strained environments,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Labeled faces in the wild: A database for studying face recognition in uncon- strained environments,

Reference 22

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This paper cites Medmnist v2: A large-scale lightweight benchmark for 2d and 3d biomedical image classification,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Medmnist v2: A large-scale lightweight benchmark for 2d and 3d biomedical image classification,

Reference 23

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Rouge: A package for automatic evaluation of summaries,

Reference 24

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This paper cites Bertscore: Evaluating text generation with bert,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Bertscore: Evaluating text generation with bert,

Reference 25

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This paper cites Zero2text: Zero-training embedding inversion via la- tent space optimization,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Zero2text: Zero-training embedding inversion via la- tent space optimization,

Reference 26

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This paper cites Zsinvert: Zero-shot text reconstruction from embeddings,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Zsinvert: Zero-shot text reconstruction from embeddings,

Reference 27

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This paper cites Understanding and mitigating the threat of vec2text to dense retrieval systems,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Understanding and mitigating the threat of vec2text to dense retrieval systems,

Reference 28

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Overview of imageclefmedical 2025–medical concept detection and interpretable caption generation,

Reference 29

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This paper cites Labeled faces in the wild: A database for studying face recognition in uncon- strained environments,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Labeled faces in the wild: A database for studying face recognition in uncon- strained environments,

Reference 30

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Medmnist v2 – a large-scale lightweight benchmark for 2d and 3d biomedical image classification,

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This paper cites GaussDB-Vector: A large-scale persistent real-time vector database for LLM applications,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases GaussDB-Vector: A large-scale persistent real-time vector database for LLM applications,

Reference 32

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This paper cites Chakraborttii,Improving performance of solid state drives using machine learning.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Chakraborttii,Improving performance of solid state drives using machine learning

Reference 34

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Information leakage in embedding models,

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems

Reference 36

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Hnsw index deletion evaluation: Persistent storage ar- tifacts,

Reference 37

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Memtrust: A zero- trust architecture for unified ai memory system,

Reference 38

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Forgetful but Faithful: A Cognitive Memory Architecture and Benchmark for Privacy-Aware Generative Agents,

Reference 39

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Towards making systems forget with machine unlearning,

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Making ai forget you: Data deletion in machine learning,

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Beyond data erasure: The eu’s convoluted approach to model deletion,

Reference 42

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Membership inference attacks against machine learning models,

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Vanish: Increasing data privacy with self-destructing data,

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases The ephemerizer: Making data disappear,

Reference 45

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Secure deletion of data from magnetic and solid-state memory,

Reference 46

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection,

Reference 47

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This paper cites Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/uthealth shared task track 1,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/uthealth shared task track 1,

Reference 48

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This paper cites Identification of protected health information in clinical notes using neural networks,.

Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Identification of protected health information in clinical notes using neural networks,

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Ghost Vectors: Soft-Deleted Embeddings Remain Reconstructible in HNSW Vector Databases Cloaked ai: Encryption sdk for protecting ai embeddings in vector databases

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