Encrypted Vector Database

The database constructs and traverses approximate nearest neighbor (ANN) index structures—such as Hierarchical Navigable Small Worlds (HNSW) graphs—directly over encrypted vector representations. The server never observes plaintext embeddings. This is achieved through cryptographic primitives like Searchable Symmetric Encryption (SSE) or Order-Revealing Encryption (ORE), which allow distance comparisons to be performed on ciphertexts without decryption.

• Mechanism: Index nodes store encrypted vectors; edge traversal decisions rely on encrypted distance comparisons.
• Trade-off: Ciphertext indexing introduces computational overhead of 10-100x compared to plaintext ANN search, depending on the encryption scheme.
• Example: A healthcare consortium queries an encrypted database of patient genomic embeddings hosted on a public cloud, retrieving similar cases without the cloud provider accessing any protected health information.

Similarity scoring between an encrypted query vector and encrypted stored vectors is performed using homomorphic encryption (HE). The server computes the distance metric—typically cosine similarity or Euclidean distance—directly on ciphertexts. The result remains encrypted and is only decipherable by the client holding the private key.

• Fully Homomorphic Encryption (FHE) supports arbitrary distance functions but incurs high latency.
• Partially Homomorphic Encryption (PHE) or Somewhat Homomorphic Encryption (SHE) is often preferred, optimized for the specific arithmetic operations required by the distance metric.
• Real-world application: A financial institution submits an encrypted embedding of a fraudulent transaction pattern; the database returns the top-k nearest neighbor matches without ever decrypting the query or the stored transaction embeddings.

The data owner retains exclusive control over cryptographic keys. Encryption and decryption of vectors occur exclusively within a trusted client library or a Trusted Execution Environment (TEE) on the client side. The database server is treated as a fully untrusted infrastructure component that only handles ciphertexts.

• Key hierarchy: A master key encrypts per-index or per-collection keys, enabling granular access revocation.
• Zero-Knowledge Proofs (ZKPs) can be integrated to verify that the server performed the correct encrypted computation without revealing the data.
• Operational model: This architecture aligns with Zero-Trust AI Networking principles, where no implicit trust is granted to the storage or compute layer.

Forward-looking encrypted vector databases adopt lattice-based cryptography to ensure long-term confidentiality. Schemes built on Ring Learning With Errors (RLWE) problems provide both homomorphic properties and resistance to cryptanalytic attacks from large-scale quantum computers.

• Rationale: Stored embeddings represent intellectual property and sensitive data patterns. An adversary harvesting ciphertexts today could decrypt them retroactively once cryptographically relevant quantum computers emerge.
• Implementation: Post-Quantum Cryptography (PQC) standards, such as CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for signatures, are integrated into the encryption layer.
• Hybrid mode: Many systems deploy a hybrid approach combining classical elliptic-curve cryptography with lattice-based schemes during the transition period.

Multiple mutually distrusting parties can jointly query an encrypted vector database without revealing their individual query vectors to one another. This is enabled by Secure Multi-Party Computation (SMPC) protocols, which distribute the query embedding as secret shares across the participating nodes.

• Protocol flow: Each party holds a share of the query vector. The database computes the similarity function over the secret-shared query and encrypted stored vectors using Garbled Circuits or homomorphic operations.
• Use case: Competing pharmaceutical companies collaboratively search a shared encrypted database of molecular embeddings to identify promising drug candidates without exposing their proprietary query compounds.
• Integration: SMPC layers are often combined with Differential Privacy to add calibrated noise to the final result set, preventing membership inference attacks.

To reduce the performance penalty of pure cryptographic approaches, encrypted vector databases can leverage Trusted Execution Environments (TEEs) such as Intel SGX or AMD SEV. Vectors are decrypted only within the hardware-enforced enclave for distance computation, then re-encrypted before leaving the CPU package.

• Remote Attestation: The client cryptographically verifies the integrity of the enclave's code before provisioning decryption keys.
• Side-Channel Mitigation: Enclave code must be hardened against side-channel attack mitigation techniques to prevent leakage through memory access patterns or timing.
• Hybrid architecture: TEEs handle the compute-intensive similarity scoring, while homomorphic encryption secures data in transit and at rest outside the enclave.

A cryptographic method enabling computation directly on ciphertext. An encrypted vector database using HE can perform similarity search operations on encrypted embeddings without ever decrypting them. The server computes over encrypted data and returns encrypted results, which only the client can decrypt.

• Partially Homomorphic Encryption (PHE): Supports only addition or multiplication, not both.
• Somewhat Homomorphic Encryption (SHE): Supports limited operations before noise overwhelms the ciphertext.
• Fully Homomorphic Encryption (FHE): Supports arbitrary computation but incurs significant computational overhead (10,000x–1,000,000x slower than plaintext).

Practical encrypted vector databases often use leveled HE schemes like CKKS, which support a predetermined number of multiplications and are optimized for approximate arithmetic on real numbers—ideal for vector dot products and distance calculations.

A graph-based indexing algorithm that constructs a multi-layered proximity graph to achieve logarithmic time complexity for approximate nearest neighbor (ANN) search. HNSW is the dominant indexing structure in modern vector databases due to its superior query performance and recall.

• Construction: Builds a hierarchical graph where upper layers contain long-range edges for coarse navigation, and lower layers contain short-range edges for fine-grained search.
• Search: Begins at the top layer, greedily traverses toward the query vector, then descends to lower layers for refinement.
• Trade-off: Higher M (edges per node) improves recall but increases memory footprint.

When combined with encryption, the graph traversal logic must be adapted to operate on encrypted distance comparisons, often using order-revealing encryption or secure two-party computation protocols.

A hardware-enforced secure area within a processor that guarantees confidentiality and integrity of code and data loaded inside it. TEEs offer a pragmatic alternative to pure cryptographic approaches for encrypted vector databases.

• Intel SGX: Creates hardware-encrypted enclaves in memory, isolating vector indexes and embeddings from the host OS and hypervisor.
• AMD SEV-SNP: Provides encrypted virtual machines with integrity protection, suitable for larger in-memory vector stores.
• NVIDIA Confidential Computing: Extends TEE protection to GPU memory, enabling encrypted vector operations on GPU-accelerated indexes.

Unlike HE, TEEs impose minimal computational overhead (typically <5%) but require trust in the hardware vendor. Remote attestation verifies the enclave's identity before provisioning encryption keys or sensitive vectors.

A vector compression technique that decomposes high-dimensional vectors into smaller sub-vectors and quantizes each independently using a learned codebook. PQ is essential for encrypted vector databases operating under memory constraints.

• Mechanism: A 768-dimensional vector is split into 96 sub-vectors of 8 dimensions each. Each sub-vector is mapped to the nearest centroid from a pre-trained codebook of 256 entries.
• Storage reduction: A 768-dim float32 vector (3,072 bytes) compresses to 96 bytes (96 × 1 byte centroid IDs).
• Distance computation: Uses Asymmetric Distance Computation (ADC)—the query vector remains uncompressed while database vectors are quantized, preserving accuracy.

In encrypted settings, PQ codebooks can be trained on plaintext data client-side, then the compressed codes are encrypted before upload, dramatically reducing the ciphertext expansion factor.

A cryptographic primitive enabling a client to outsource encrypted data to an untrusted server and later issue search queries that the server executes without decrypting the data. SSE forms the foundation for keyword-searchable encrypted databases and extends to vector similarity search.

• Static SSE: The encrypted dataset is fixed after initial upload; only search queries are supported.
• Dynamic SSE: Supports updates (insertions, deletions) to the encrypted dataset without full re-encryption.
• Leakage profiles: SSE schemes inherently leak access patterns (which encrypted records match a query) and search patterns (whether two queries are identical). Advanced schemes use Oblivious RAM (ORAM) to hide access patterns at higher overhead.

For vector databases, SSE must be adapted to support approximate rather than exact matching, typically by combining with locality-sensitive hashing or order-revealing encryption for distance comparisons.

A mathematical framework that injects calibrated statistical noise into query results to prevent re-identification of individual records. In encrypted vector databases, differential privacy protects against inference attacks that could reconstruct embeddings from query responses.

• ε (epsilon): The privacy budget parameter. Lower values (ε < 1) provide stronger privacy but add more noise.
• δ (delta): The probability of catastrophic privacy failure; typically set to 1/N where N is the dataset size.
• Mechanisms: The Gaussian mechanism adds noise proportional to the L2 sensitivity of the query function. The Laplace mechanism is used for L1 sensitivity.

Applied to vector search, differential privacy can obscure whether a specific embedding exists in the database or mask the exact distance values returned, preventing membership inference attacks while preserving approximate ranking.