# Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing

arXiv: https://arxiv.org/abs/1703.05455 · HTML: https://mlsanity.com/paper/1703.05455/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 21 of 21)

- [An Overview of Privacy-enhancing Technologies in Biometric Recognition](https://mlsanity.com/paper/2206.10465/) (2022)
- [Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques](https://mlsanity.com/paper/2110.05044/) (2021)
- [MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron](https://mlsanity.com/paper/2204.11054/) (2022)
- [IDFace: Face Template Protection for Efficient and Secure Identification](https://mlsanity.com/paper/2507.12050/) (2025)
- [Benchmarking of Cancelable Biometrics for Deep Templates](https://mlsanity.com/paper/2302.13286/) (2023)
- [Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns](https://mlsanity.com/paper/2310.03091/) (2023)
- [Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition](https://mlsanity.com/paper/2404.02696/) (2024)
- [CBRW: A Novel Approach for Cancelable Biometric Template Generation based on](https://mlsanity.com/paper/2404.16739/) (2024)
- [ColorVein: Colorful Cancelable Vein Biometrics](https://mlsanity.com/paper/2504.14253/) (2025)
- [Random Hash Code Generation for Cancelable Fingerprint Templates using Vector Permutation and Shift-order Process](https://mlsanity.com/paper/2105.10227/) (2021)
- [A Generalized Approach for Cancellable Template and Its Realization for Minutia Cylinder-Code](https://mlsanity.com/paper/2203.01095/) (2022)
- [Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models](https://mlsanity.com/paper/2209.11020/) (2022)
- [Composite Fixed-Length Ordered Features for Palmprint Template Protection with Diminished Performance Loss](https://mlsanity.com/paper/2211.04884/) (2022)
- [Secure and Privacy Preserving Proxy Biometrics Identities](https://mlsanity.com/paper/2212.10812/) (2022)
- [Hypersphere Secure Sketch Revisited: Probabilistic Linear Regression Attack on IronMask in Multiple Usage](https://mlsanity.com/paper/2409.12884/) (2024)
- [Cryptanalysis of Cancelable Biometrics Vault](https://mlsanity.com/paper/2501.05786/) (2025)
- [FaceCloak: Learning to Protect Face Templates](https://mlsanity.com/paper/2504.06131/) (2025)
- [Secure and Scalable Face Retrieval via Cancelable Product Quantization](https://mlsanity.com/paper/2509.00781/) (2025)
- [Model Inversion meets Cryptographic Fuzzy Extractors](https://mlsanity.com/paper/2510.25687/) (2025)
- [Realistic Face Reconstruction from Facial Embeddings via Diffusion Models](https://mlsanity.com/paper/2602.13168/) (2026)
- [Binding Biometrics with AI Agent Identifiers for Delegation of Authority](https://mlsanity.com/paper/2608.04292/) (2026)

## Abstract (arXiv)

In this paper, we propose a ranking based locality sensitive hashing inspired two-factor cancelable biometrics, dubbed "Index-of-Max" (IoM) hashing for biometric template protection. With externally generated random parameters, IoM hashing transforms a real-valued biometric feature vector into discrete index (max ranked) hashed code. We demonstrate two realizations from IoM hashing notion, namely Gaussian Random Projection based and Uniformly Random Permutation based hashing schemes. The discrete indices representation nature of IoM hashed codes enjoy serveral merits. Firstly, IoM hashing empowers strong concealment to the biometric information. This contributes to the solid ground of non-invertibility guarantee. Secondly, IoM hashing is insensitive to the features magnitude, hence is more robust against biometric features variation. Thirdly, the magnitude-independence trait of IoM hashing makes the hash codes being scale-invariant, which is critical for matching and feature alignment. The experimental results demonstrate favorable accuracy performance on benchmark FVC2002 and FVC2004 fingerprint databases. The analyses justify its resilience to the existing and newly introduced security and privacy attacks as well as satisfy the revocability and unlinkability criteria of cancelable biometrics.