Understanding the Error Sensitivity of Privacy-Aware Computing

• URL: http://arxiv.org/abs/2506.07957v1
• Date: Mon, 09 Jun 2025 17:27:40 GMT
• Title: Understanding the Error Sensitivity of Privacy-Aware Computing
• Authors: Matías Mazzanti, Esteban Mocskos, Augusto Vega, Pradip Bose,
• Abstract summary: Homomorphic Encryption (HE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation.<n>In this work, we motivate a thorough discussion regarding the sensitivity of HE applications to bit faults and provide a detailed error characterization study of CKKS (Cheon-Kim-Kim-Song)<n> CKKS is one of the most popular HE schemes due to its fixed-point arithmetic support for AI and machine learning applications.
• Score: 0.5494759889025727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Homomorphic Encryption (HE) enables secure computation on encrypted data without decryption, allowing a great opportunity for privacy-preserving computation. In particular, domains such as healthcare, finance, and government, where data privacy and security are of utmost importance, can benefit from HE by enabling third-party computation and services on sensitive data. In other words, HE constitutes the "Holy Grail" of cryptography: data remains encrypted all the time, being protected while in use. HE's security guarantees rely on noise added to data to make relatively simple problems computationally intractable. This error-centric intrinsic HE mechanism generates new challenges related to the fault tolerance and robustness of HE itself: hardware- and software-induced errors during HE operation can easily evade traditional error detection and correction mechanisms, resulting in silent data corruption (SDC). In this work, we motivate a thorough discussion regarding the sensitivity of HE applications to bit faults and provide a detailed error characterization study of CKKS (Cheon-Kim-Kim-Song). This is one of the most popular HE schemes due to its fixed-point arithmetic support for AI and machine learning applications. We also delve into the impact of the residue number system (RNS) and the number theoretic transform (NTT), two widely adopted HE optimization techniques, on CKKS' error sensitivity. To the best of our knowledge, this is the first work that looks into the robustness and error sensitivity of homomorphic encryption and, as such, it can pave the way for critical future work in this area.

Related papers

• Characterizing the Sensitivity to Individual Bit Flips in Client-Side Operations of the CKKS Scheme [0.0]
  Homomorphic Encryption (HE) enables computation on encrypted data without decryption, making it a cornerstone of privacy-preserving computation in untrusted environments.<n>HE sees growing adoption in sensitive applications such as secure machine learning and confidential data analysis ensuring its robustness against errors becomes critical.
  arXiv  Detail & Related papers  (2025-07-28T14:42:09Z)
• 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)
• DataSeal: Ensuring the Verifiability of Private Computation on Encrypted Data [14.21750921409931]
  We introduce DataSeal, which combines the low overhead of the algorithm-based fault tolerance (ABFT) technique with the confidentiality of Fully Homomorphic Encryption (FHE) DataSeal achieves much lower overheads for providing computation verifiability for FHE than other techniques that include MAC, ZKP, and TEE.
  arXiv  Detail & Related papers  (2024-10-19T21:19:39Z)
• Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors [0.9831489366502302]
  Homomorphic encryption schemes are promising solutions as they enable the processing and execution of operations on IoT data while still encrypted. We propose a novel privacy-preserving anomaly detection solution designed for homomorphically encrypted data generated by IoT devices.
  arXiv  Detail & Related papers  (2024-03-14T12:11:25Z)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• Hyperdimensional Computing as a Rescue for Efficient Privacy-Preserving Machine Learning-as-a-Service [9.773163665697057]
  Homomorphic encryption (HE) is a promising technique to address this adversity. With HE, the service provider can take encrypted data as a query and run the model without decrypting it. We show hyperdimensional computing can be a rescue for privacy-preserving machine learning over encrypted data.
  arXiv  Detail & Related papers  (2023-08-17T00:25:17Z)
• Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning [63.45532264721498]
  Self-supervised learning is an emerging technique to pre-train encoders using unlabeled data. We perform first systematic, principled measurement study to understand whether and when a pretrained encoder can address the limitations of secure or privacy-preserving supervised learning algorithms.
  arXiv  Detail & Related papers  (2022-12-06T21:35:35Z)
• SoK: Privacy-preserving Deep Learning with Homomorphic Encryption [2.9069679115858755]
  homomorphic encryption (HE) can be performed on encrypted data without revealing its content. We take an in-depth look at approaches that combine neural networks with HE for privacy preservation. We find numerous challenges to HE based privacy-preserving deep learning such as computational overhead, usability, and limitations posed by the encryption schemes.
  arXiv  Detail & Related papers  (2021-12-23T22:03:27Z)
• Reinforcement Learning on Encrypted Data [58.39270571778521]
  We present a preliminary, experimental study of how a DQN agent trained on encrypted states performs in environments with discrete and continuous state spaces. Our results highlight that the agent is still capable of learning in small state spaces even in presence of non-deterministic encryption, but performance collapses in more complex environments.
  arXiv  Detail & Related papers  (2021-09-16T21:59:37Z)
• Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
  Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
  arXiv  Detail & Related papers  (2020-06-17T18:14:30Z)
• Cryptotree: fast and accurate predictions on encrypted structured data [0.0]
  Homomorphic Encryption (HE) is acknowledged for its ability to allow computation on encrypted data, where both the input and output are encrypted. We propose Cryptotree, a framework that enables the use of Random Forests (RF), a very powerful learning procedure compared to linear regression.
  arXiv  Detail & Related papers  (2020-06-15T11:48:01Z)
• CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
  We present a framework for privacy-preserving inference of sum-product networks (SPNs) CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
  arXiv  Detail & Related papers  (2020-02-03T14:49:18Z)

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.