To achieve confidentiality, integrity, authentication, and non-repudiation simultaneously, the concept of signcryption was introduced by combining encryption and a signature in a single scheme. Certificate-based encryption schemes are designed to resolve the key escrow problem of identity-based encryption, as well as to simplify the certificate management problem in traditional public key cryptosystems. In this paper, we propose a new certificate-based signcryption scheme that has been proved to be secure against adaptive chosen ciphertext attacks and existentially unforgeable against chosen-message attacks in the random oracle model. Our scheme is not based on pairing and thus is efficient and practical. Furthermore, it allows a signcrypted message to be immediately verified by the public key of the sender. This means that verification and decryption of the signcrypted message are decoupled. To the best of our knowledge, this is the first signcryption scheme without pairing to have this feature.Keywords: Signcryption, certificate-based signcryption, certificate-based public key cryptography. Manuscript received Nov. 15, 2015; revised Mar. 3, 2016, accepted Apr. 19, 2016 I. IntroductionAuthentication is a fundamental block of a secure system. Basically, it is a process for verifying that the identity of an entity belongs to a human or a device. For example, in the authentication process, a certificate in traditional public key cryptography (PKC) is usually used to prove that a public key belongs to a specific user. However, a public key infrastructure (PKI) that supports a traditional PKC has issues, such as complex installation and maintenance processes, issuance, distribution, and a revocation of the certificates.Although the authentication process seems to be irreplaceable, some public key cryptography models have been proposed in which the certificate is eliminated. In 1984, Shamir proposed the first concept of identity-based public key cryptography (ID-PKC) [1]. This scheme shows a great improvement, that is, it does not require PKI because the public key is an identity (for example, email, ID number, driver license number, and so on). In ID-PKC, a private key generator (PKG) uses a master secret key to generate all private keys for its users. The PKG requires secure channels to deliver the private keys to users securely. Although the improvement in ID-PKC is significant, some architectural issues still remain: (1) A secure channel to deliver the private keys is significantly costly to implement. (2) The PKG can impersonate any user at any time because it knows the private keys of all users, which is called the key escrow problem. This issue is unacceptable in certain cases such as legal applications because the PKG cannot guarantee non-repudiation. (3) Finally, the security of the whole system depends on the secrecy of the master secret key. If the PKG is compromised and the master key is revealed, the whole system is affected.To overcome the drawbacks of the traditional PKC and ID-PKC, the first concept ...
Tài liệu kỹ thuật phục vụ cuộc họp Hội đồng khoa học của VIASM ngày 13/11/2020 (No. VIASM-AISDL-20.01) Tại VIASM, Ngõ 157 Chùa Láng, Láng Thượng, Đống Đa, Hà Nội. Trong quãng thời gian từ ngày 23/12/2019 đến 5/11/2020, cơ sở dữ liệu SciMath đã ghi nhận 8372 công bố, 3058 tác giả (trong đó 1566 tác giả Việt Nam và 1492 tác giả nước ngoài), 1267 tạp chí/ sách/ kỉ yếu.
Privacy of machine learning models is one of the remaining challenges that hinder the broad adoption of Artificial Intelligent (AI). This paper considers this problem in the context of image datasets containing faces. Anonymization of such datasets is becoming increasingly important due to their central role in the training of autonomous cars, for example, and the vast amount of data generated by surveillance systems. While most prior work de-identifies facial images by modifying identity features in pixel space, we instead project the image onto the latent space of a Generative Adversarial Network (GAN) model, find the features that provide the biggest identity disentanglement, and then manipulate these features in latent space, pixel space, or both. The main contribution of the paper is the design of a feature-preserving anonymization framework, StyleID, which protects the individuals’ identity, while preserving as many characteristics of the original faces in the image dataset as possible. As part of the contribution, we present a novel disentanglement metric, three complementing disentanglement methods, and new insights into identity disentanglement. StyleID provides tunable privacy, has low computational complexity, and is shown to outperform current state-of-the-art solutions.
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