Statistical Analysis and Optimization of the MFA Protecting Private Keys

• URL: http://arxiv.org/abs/2603.05978v1
• Date: Fri, 06 Mar 2026 07:20:41 GMT
• Title: Statistical Analysis and Optimization of the MFA Protecting Private Keys
• Abstract summary: Loss of private keys can have catastrophic consequences.<n>In this paper, we focus on generating ephemeral keys to protect private keys.<n>We propose a novel bit-truncation method in which the most significant bits (MSBs) of response values derived from facial features are removed.
• Score: 25.652674188095883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the current information age, asymmetrical cryptography is widely used to protect information and financial transactions such as cryptocurrencies. The loss of private keys can have catastrophic consequences; therefore, effective MFA schemes are needed. In this paper, we focus on generating ephemeral keys to protect private keys. We propose a novel bit-truncation method in which the most significant bits (MSBs) of response values derived from facial features in a template-less biometric scheme are removed, significantly improving both accuracy and security. A statistical analysis is presented to optimize an MFA comprising at least three factors: template-less biometrics, an SRAM PUF-based token, and passwords. The results show a reduction in both false-reject and false-acceptance rates, and the generation of error-free ephemeral keys.

Related papers

• Provably Protecting Fine-Tuned LLMs from Training Data Extraction [27.190752375819972]
  Fine-tuning large language models (LLMs) on sensitive datasets raises privacy concerns.<n>We propose SCP-$_r$, a Near Access Freeness (NAF)-based algorithm that operates on relative probabilities.<n> SCP-$_r$ achieves orders-of-magnitude better theoretical bounds than existing NAF based methods.
  arXiv  Detail & Related papers  (2026-01-31T12:18:36Z)
• Secure Multi-Key Homomorphic Encryption with Application to Privacy-Preserving Federated Learning [18.399679909318394]
  We identify a critical security vulnerability in the CDKS scheme when applied to multiparty secure computation tasks.<n>We propose a new scheme, SMHE, which incorporates a novel masking mechanism into the multi-key BFV and CKKS frameworks.<n>We implement a PPFL application using SMHE and demonstrate it provides significantly improved security with only a modest overhead in runtime evaluation.
  arXiv  Detail & Related papers  (2025-06-25T03:28:25Z)
• Bidirectional Biometric Authentication Using Transciphering and (T)FHE [2.423735225769664]
  Biometric authentication systems pose privacy risks, as leaked templates such as iris or fingerprints can lead to security breaches.<n>We propose the Bidirectional Transciphering Framework (BTF) to enable efficient and privacy-preserving biometric authentication.<n>BTF addresses three core deployment challenges: reducing the size of returned FHE ciphertexts, preventing clients from falsely reporting successful authentication, and enabling scalable, centralized FHE key management.
  arXiv  Detail & Related papers  (2025-06-15T10:34:57Z)
• Quantize What Counts: More for Keys, Less for Values [63.51476878610841]
  Large Language Models (LLMs) suffer inference-time memory bottlenecks dominated by the attention Key-Value ( KV) cache.<n>This paper proposes two theorems that anchor mixed-precision KV quantization in the intrinsic geometry of Transformer models.
  arXiv  Detail & Related papers  (2025-02-20T22:24:27Z)
• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem.<n>These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem.<n>We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Cryptanalysis of Cancelable Biometrics Vault [0.552480439325792]
  Cancelable Biometrics (CB) stands for a range of biometric transformation schemes combining biometrics with user specific tokens to generate secure templates.<n>In biometrics, a key-binding scheme is used for protecting a cryptographic key using a biometric data.<n>Our cryptanalysis introduces a new perspective by uncovering the CBV scheme's revocability and linkability vulnerabilities.
  arXiv  Detail & Related papers  (2025-01-10T08:36:59Z)
• Differentially Private Random Feature Model [47.35176457481132]
  We produce a differentially private random feature model for privacy-preserving kernel machines.<n>We show that our method preserves privacy and derive a generalization error bound for the method.
  arXiv  Detail & Related papers  (2024-12-06T05:31:08Z)
• Pseudo-Probability Unlearning: Towards Efficient and Privacy-Preserving Machine Unlearning [59.29849532966454]
  We propose PseudoProbability Unlearning (PPU), a novel method that enables models to forget data to adhere to privacy-preserving manner. Our method achieves over 20% improvements in forgetting error compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-11-04T21:27:06Z)
• WiFaKey: Generating Cryptographic Keys from Face in the Wild [25.144030737846276]
  Deriving a unique cryptographic key from biometric measurements is a challenging task due to the existing noise gap between the biometric measurements and error correction coding. We propose a novel biometric cryptosystem named WiFaKey, for generating cryptographic keys from face in unconstrained settings.
  arXiv  Detail & Related papers  (2024-07-20T08:31:06Z)
• Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano [83.5933307263932]
  We study data reconstruction attacks for discrete data and analyze it under the framework of hypothesis testing. We show that if the underlying private data takes values from a set of size $M$, then the target privacy parameter $epsilon$ can be $O(log M)$ before the adversary gains significant inferential power.
  arXiv  Detail & Related papers  (2022-10-24T23:50:12Z)
• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models [69.66654761324702]
  We propose a simple yet effective just-fine-tune-twice privacy mechanism to achieve SDP for large Transformer-based language models. Experiments show that our models achieve strong performance while staying robust to the canary insertion attack.
  arXiv  Detail & Related papers  (2022-04-15T22:36:55Z)
• Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers [3.3549957463189095]
  Gait data captured by inertial sensors have demonstrated promising results on user authentication. Most existing approaches stored the enrolled gait pattern insecurely for matching with the pattern, thus, posed critical security and privacy issues. We present a gait cryptosystem that generates from gait data the random key for user authentication, meanwhile, secures the gait pattern.
  arXiv  Detail & Related papers  (2021-08-05T06:34:42Z)
• Recovering AES Keys with a Deep Cold Boot Attack [91.22679787578438]
  Cold boot attacks inspect the corrupted random access memory soon after the power has been shut down. In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys. Our results show that our methods outperform the state of the art attack methods by a very large margin.
  arXiv  Detail & Related papers  (2021-06-09T07:57:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.