Existing reversible data hiding methods in encrypted images (RDH-EI) are primarily designed for point-to-point scenarios and not suitable for multiparty communication. To address this issue, a novel RDH-EI scheme based on multi-key (k,n)-threshold decryption (RDH-EITD) is proposed, in which a dual-phase embedding method is designed to support authentication for both the content owner and central server. In RDH-EITD, the private key of Paillier is split intonshares, which are then allocated tondistributed receivers. The image is first encrypted and then embedded with additional data to generate the marked ciphertext, which is uploaded to data server. The server can perform the 2nd-phase embedding. The dual-marked ciphertext is then distributed tonreceivers. Each receiver can extract the 2nd-phase embedded data but cannot decrypt the image individually. Only whenkout ofnreceivers submit their partially decrypted results, can the image be decrypted. Then the 1st-phase embedded data can be extracted and the original image can be recovered losslessly. Security reduction is employed to formally prove the semantic security of RDH-EITD. Experimental results demonstrate that RDH-EITD preserves (k,n)-threshold decryption, enabling resistance againstk− 1 collusion attacks and tolerance ofn−kfailures. The dual-phase embedding achieves embedding rates of λ − 9 bpp (bits per pixel) and 14 bpp respectively with security parameter λ. For an image of lengthLin one-to-ncommunication scenarios, the time complexity of RDH-EITD reachesO(L(λ3+k2)), outperforming existing Paillier-based point-to-point solutions.

Federated learning (FL) allows for collaborative training without centralizing data, but concerns regarding model privacy leakage, intellectual property theft and poisoning attacks have hindered its development. To mitigate such risks, this paper proposes embedded message authentication code technology (EMAC) to integrate encryption, digital signatures, and watermark functions for model security. In EMAC, the authentication data is embedded into the model ciphertext using reversible data hiding after encryption. The marked ciphertext supports data extraction for subsequent authentication and lossless decryption for testing and training simultaneously. Based on EMAC, a novel FL with security authentication and traceability of poisoning (FL-SATP) is proposed, which integrates privacy protection, identity authentication and poisoning traceability into FL. The poisoner tracing is designed to detect and identify poisoners retrospectively based on the practical performance of trained or aggregated models, thus removing the malicious users' model and deterring poisoning behaviors. Theoretical analysis and experimental results demonstrate that FL-SATP could ensure the confidentiality of the model content, the availability of model function, and that when more than half of the users are benign, the proposed method can accurately and efficiently pinpoint all malicious poisoners in FL.

Abstract Video steganography has the potential to be used to convey illegal information, and video steganalysis is a vital tool to detect the presence of this illicit act. Currently, all the motion vector (MV)‐based video steganalysis algorithms extract feature sets directly from the MVs, but ignoring the embedding operation may perturb the statistical distribution of other video encoding elements, such as the skipped macroblocks (no direct MVs). This paper proposes a novel 11‐dimensional feature set to detect MV‐based video steganography based on the above observation. The proposed feature is extracted based on the skipped macroblocks by recompression calibration. Specifically, the feature consists of two components. The first is the probability distribution of motion vector prediction (MVP) difference, and the second is the probability distribution of partition state transfer. Extensive experiments on different conditions demonstrate that the proposed feature set achieves good detection accuracy, especially in lower embedding capacities. In addition, the loss of detection performance caused by recompression calibration using mismatched quantization parameters (QP) is within the acceptable range, so the proposed method can be used in practical scenarios.

To improve the embedding capacity of reversible data hiding in encrypted images (RDH-EI), a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit (MSB) prediction. First, according to the smoothness of the image, the image is partitioned into blocks based on adaptive quadtree partitioning, and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images. In the data embedding stage, the adaptive MSB prediction method proposed by Wang and He (2022) is improved by taking the upper-left pixel in the block as the target pixel, to predict other pixels to free up more embedding space. To the best of our knowledge, quadtree partitioning is first applied to RDH-EI. Simulation results show that the proposed method is reversible and separable, and that its average embedding capacity is improved. For gray images with a size of 512×512, the average embedding capacity is increased by 25 565 bits. For all smooth images with improved embedding capacity, the average embedding capacity is increased by about 35 530 bits.

Threshold proxy re-encryption (TPRE) can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies, and can also provide normal services even when certain proxy servers are paralyzed or damaged. A non-interactive identity-based TPRE (IB-TPRE) scheme over lattices is proposed which removes the public key certificates. To accomplish this scheme, Shamir’s secret sharing is employed twice, which not only effectively hides the delegator’s private key information, but also decentralizes the proxy power by splitting the re-encryption key. Robustness means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext share. This property is achieved by lattice-based fully homomorphic signatures. As a result, the whole scheme is thoroughly capable of resisting quantum attacks even when they are available. The security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard model. Two typical application scenarios, including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography, are presented.

Schemes of reversible data hiding in encrypted domain (RDH-ED) based on symmetric or public key encryption are mainly applied in the scenarios of end-to-end communication. To provide security guarantees for the multi-party scenarios, a RDH-ED scheme for secret image sharing based on Chinese remainder theorem (CRT) is presented. In the application of ($t$,$n$) secret image sharing, an image is first shared into$n$different shares of ciphertext. Only when not less than$t$shares obtained, can the image be reconstructed. In our scheme, additional data could be embedded into the image shares. To realize data extraction from the image shares and the reconstructed image separably, two data hiding methods are proposed: one is homomorphic difference expansion in encrypted domain (HDE-ED) that supports data extraction from the reconstructed image by utilizing the addition homomorphism of CRT secret sharing; the other is difference expansion in image shares (DE-IS) that supports the data extraction from the marked shares before image reconstruction. Experimental results demonstrate that the proposed scheme could not only maintain the security and the threshold function of secret sharing system, but also obtain a better reversibility and efficiency compared with most existing RDH-ED algorithms. The maximum embedding rate of HDE-ED could reach 0.500 bits per pixel and the average embedding rate of DE-IS could reach 0.4652 bits per pixel.

This paper proposes a fully homomorphic encryption encapsulated difference expansion (FHEE-DE) scheme for reversible data hiding in encrypted domain (RDH-ED). The homomorphic circuits and ciphertext operations are elaborated. Key-switching and bootstrapping techniques are introduced to control the ciphertext extension and decryption failure of homomorphic encryption. A key-switching based least-significant-bit (KS-LSB) data hiding method has been designed to realize data extraction directly from the encrypted domain without the private key. In application, the user first encrypts the plaintext and uploads ciphertext to the server. The server embeds additional data into the ciphertext by performing FHEE-DE data hiding and KS-LSB data hiding. Additional data can be extracted directly from the marked ciphertext by the server without the private key. The user owns the private key and can decrypt the marked ciphertext to obtain the marked plaintext. Then additional data or plaintext can be obtained from the marked plaintext by using the standard DE extraction or recovery. The server could also implement FHEE-DE recovery or extraction on the marked ciphertext to return the ciphertext of original plaintext or additional data to the user. Experimental results demonstrate that the embedding capacity and reversibility of the proposed scheme are superior to existing RDH-ED methods, and fully separability is achieved without reducing the security of encryption.

Summary In the IoT setting, many resource constrained devices outsource their collected data to the Cloud. To ensure the outsourced data has not been lost, these devices need some mechanism to check the integrity of their data. Furthermore, in some settings, ad hoc devices need to act as a group, and in this group, any member may require to verify the integrity of outsourced cloud storage. Aiming at solving this problem, in AISACCS'15, first proposed the concept of group‐based proofs of storage (GPoS). In GPoS, a group manager can authorize data owners as group members, and then these group members can outsource files to the cloud storage server; later, each member can verify the integrity of the outsourced cloud storage. They also give a concrete construction of GPoS. Unfortunately, in this paper, we find their scheme is not secure. Recently, also proposed a dynamic group‐based integrity auditing protocol for outsrouced cloud storage; we also show their scheme is not secure either. Finally, we give an improved scheme and roughly analysis to its security and performance.

Because design is an iterative and evolving process, recording and representing designer's thinking process plays an important role for design change and reuse. After giving a brief review of the previous related research, a design thinking process model is created for representing the design thinking process. Its knowledge structure consisting of design intent, design decision, design rationale and design operation is explained in detail. Based on the proposed model, a knowledge modeling language (Design Mental Modeling Language, DMML) is developed for expressing and reasoning the design thinking process explicitly. Semi-formal structure is used by DMML, which can improve both the expressiveness and computability of this language. A visual modeling system is developed for implementing DMML and an example is presented for illustration.

In Informatica 32 (2008), Ren and Gu proposed an anonymous hierarchical identity based encryption scheme based on the q-ABDHE problem with full security in the standard model. Later in Indocrypt'08, they proposed another secure hierarchical identity based encryption scheme based on the q-TBDHE problem with full security in the standard model. They claimed that their schemes have short parameters, high efficiency and tight reduction. However, in this paper we give attacks to show their schemes are not secure at all. Concretely, from any first level private key, the adversary can easily derive a “private key” which can decrypt any ciphertexts for the target identity. That is to say, a query on any first level identity is enough to decrypt any ciphertext in the system.

An audio digital signature algorithm based on principal component analysis (PCA) was presented. Wavelet packet decomposition was implemented to audio files, all the terminal coefficients of the wavelet packet decomposing tree were used as the data matrix. The matrix was chaotically encrypted, and then it was principal component analysis that to get the eigenvector. Quantized eigenvector matrix was used as the signature, and the signature was embedded into the primal audio data by using watermarking system, and at the same time ICA technology was introduced to locate the audio tamper. The experiment results confirm that the signature is sensitive to the change of the audio data and can locate the tamper.