Privacy-Preserving Traceable Functional Encryption for Inner Product

• URL: http://arxiv.org/abs/2404.04861v2
• Date: Mon, 15 Apr 2024 03:22:30 GMT
• Title: Privacy-Preserving Traceable Functional Encryption for Inner Product
• Authors: Muyao Qiu, Jinguang Han,
• Abstract summary: New primitive called traceable functional encryption for inner product (TFE-IP) has been proposed. Privacy protection of user's identities has not been considered in the existing TFE-IP schemes.
• Score: 0.3683202928838613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Functional encryption introduces a new paradigm of public key encryption that decryption only reveals the function value of encrypted data. To curb key leakage issues and trace users in FE-IP, a new primitive called traceable functional encryption for inner product (TFE-IP) has been proposed. However, the privacy protection of user's identities has not been considered in the existing TFE-IP schemes. In order to balance privacy and accountability, we propose the concept of privacy-preserving traceable functional encryption for inner product (PPTFE-IP) and give a concrete construction. Our scheme provides the following features: (1) To prevent key sharing, a user's key is bound with both his/her identity and a vector; (2) The key generation center (KGC) and a user execute a two-party secure computing protocol to generate a key without the former knowing anything about the latter's identity; (3) Each user can verify the correctness of his/her key; (4) A user can calculate the inner product of the two vectors embedded in his/her key and in a ciphertext; (5) Only the tracer can trace the identity embedded in a key. The security of our scheme is formally reduced to well-known complexity assumptions, and the implementation is conducted to evaluate its efficiency. The novelty of our scheme is to protect users' privacy and provide traceability if required.

Related papers

• Privacy-Enhanced Adaptive Authentication: User Profiling with Privacy Guarantees [0.6554326244334866]
  This paper introduces a novel privacy-enhanced adaptive authentication protocol. It dynamically adjusts authentication requirements based on real-time risk assessments. By adhering to data protection regulations such as CCPA, our protocol not only enhances security but also fosters user trust.
  arXiv  Detail & Related papers  (2024-10-27T19:11:33Z)
• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• Privacy-preserving server-supported decryption [2.2530496464901106]
  We consider encryption systems with two-out-of-two threshold decryption, where one of the parties initiates the decryption and the other one assists. Existing threshold decryption schemes disclose to the server the ciphertext that is being decrypted. We give a construction, where the identity of the ciphertext is not leaked to the server, and the client's privacy is preserved.
  arXiv  Detail & Related papers  (2024-10-25T06:47:53Z)
• A Privacy-preserving key transmission protocol to distribute QRNG keys using zk-SNARKs [2.254434034390528]
  Quantum Random Number Generators can provide high-quality keys for cryptographic algorithms. Existing Entropy-as-a-Service solutions require users to trust the central authority distributing the key material. We present a novel key transmission protocol that allows users to obtain cryptographic material generated by a QRNG in such a way that the server is unable to identify which user is receiving each key.
  arXiv  Detail & Related papers  (2024-01-29T14:00:37Z)
• Blockchain-based Privacy-Preserving Public Key Searchable Encryption with Strong Traceability [8.866154405581012]
  We propose a blockchain-based privacy-preserving PKSE with strong traceability (BP3KSEST) scheme. Our scheme provides the following features: (1) authorized users can authenticate to trapdoor generation center and obtain trapdoors without releasing identities and keywords; (2) when data users misbehave in the system, the trusted third party (TTP) can trace both their identities and the keywords which they searched; (3) trapdoor query records are unforgeable; (4) trapdoor query records are immutable because records are stored in blockchain.
  arXiv  Detail & Related papers  (2023-12-28T10:58:14Z)
• PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via Secure Flow [69.78820726573935]
  We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model. In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one.
  arXiv  Detail & Related papers  (2023-07-18T10:55:54Z)
• RiDDLE: Reversible and Diversified De-identification with Latent Encryptor [57.66174700276893]
  This work presents RiDDLE, short for Reversible and Diversified De-identification with Latent Encryptor. Built upon a pre-learned StyleGAN2 generator, RiDDLE manages to encrypt and decrypt the facial identity within the latent space.
  arXiv  Detail & Related papers  (2023-03-09T11:03:52Z)
• Revocable Cryptography from Learning with Errors [61.470151825577034]
  We build on the no-cloning principle of quantum mechanics and design cryptographic schemes with key-revocation capabilities. We consider schemes where secret keys are represented as quantum states with the guarantee that, once the secret key is successfully revoked from a user, they no longer have the ability to perform the same functionality as before.
  arXiv  Detail & Related papers  (2023-02-28T18:58:11Z)
• Public Key Encryption with Secure Key Leasing [24.410596031297242]
  We introduce the notion of public key encryption with secure key leasing (PKE-SKL) Our notion is similar in spirit to the notion of secure software leasing (SSL) introduced by Ananth and La Placa (Eurocrypt 2021) In more detail, our adversary is not restricted to use an honest evaluation algorithm to run pirated software.
  arXiv  Detail & Related papers  (2023-02-22T21:37:57Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• 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)

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.