Secret Sharing Research Articles

EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party

Federated learning (FL) is a machine learning setting which allows multiple participants collaboratively to train a model under the orchestration of a server without disclosing their local data. Vertical federated learning (VFL) is a special structure in FL. It handles the situation where participants have the same ID space but different feature spaces. In order to guarantee the security and privacy of the local data of each participant, homomorphic encryption (HE) is often used to transmit intermediate parameters or data during the training process. In most VFL frameworks, a trusted third-party server is necessary because the plaintexts of the parameters need to be revealed for the computation. However, it is hard to find such a credible entity in the real world. Existing methods for solving this problem are either communication-intensive or unsuitable for multi-party scenarios. By combining secret sharing (SS) and HE, we propose a novel VFL framework without any trusted third parties called EFMVFL. It allows intermediate parameters to be transmitted among multiple parties without revealing the plaintexts. EFMVFL is applicable to generalized linear models (GLMs) and supports flexible expansion to multiple participants. Extensive experiments under Logistic Regression and Poisson Regression show that our framework is outstanding in communication (reduced by 3.2×– 6.8×) and efficiency (accelerated by 1.6×– 3.1×).

Retracted: Privacy-Oriented Transaction for Public Blockchain via Secret Sharing

Retracted: Privacy-Oriented Transaction for Public Blockchain via Secret Sharing

Earth vitality: An integrated framework for tracking Earth sustainability

The Anthropocene era is characterized by the escalating impact of human activities on the environment, as well as the increasingly complex interactions among various components of the Earth system. These factors greatly affect the Earth’s evolutionary trajectory. Despite notable strides in sustainable development practices worldwide, it remains unclear to what extent we have achieved Earth sustainability. Consequently, there is a pressing need to enhance conceptual and methodological frameworks to measure sustainability progress accurately. To address this need, we developed an Earth Vitality Framework that aids in tracking the Earth sustainability progress by considering interactions between spheres, recognizing the equal relationship between humans and nature, and presenting a threshold scheme for all measures. We applied this framework at global and national scales to demonstrate its usefulness. Our findings reveal that the current Earth Vitality Index is 63.74, indicating that the Earth is in a “weak” vitality. Irrational social institutions, unsatisfactory life experiences and the poor state of the biosphere and hydrosphere have remarkably affected the Earth vitality. Additionally, inequality exists between high-income and low-income countries. Although most of the former exhibit poor human-nature interaction, all of them enjoy good human well-being, while the opposite is true for the latter. Finally, we summarize the challenges and possible options for enhancing the Earth vitality in terms of coping with spillover effects, tipping cascades, feedback, and heterogeneity.

Reconstruction Guided Meta-Learning for Few Shot Open Set Recognition.

In many applications, we are constrained to learn classifiers from very limited data (few-shot classification). The task becomes even more challenging if it is also required to identify samples from unknown categories (open-set classification). Learning a good abstraction for a class with very few samples is extremely difficult, especially under open-set settings. As a result, open-set recognition has received limited attention in the few-shot setting. However, it is a critical task in many applications like environmental monitoring, where the number of labeled examples for each class is limited. Existing few-shot open-set recognition (FSOSR) methods rely on thresholding schemes, with some considering uniform probability for open-class samples. However, this approach is often inaccurate, especially for fine-grained categorization, and makes them highly sensitive to the choice of a threshold. To address these concerns, we propose Reconstructing Exemplar-based Few-shot Open-set ClaSsifier (ReFOCS). By using a novel exemplar reconstruction-based meta-learning strategy ReFOCS streamlines FSOSR eliminating the need for a carefully tuned threshold by learning to be self-aware of the openness of a sample. The exemplars, act as class representatives and can be either provided in the training dataset or estimated in the feature domain. By testing on a wide variety of datasets, we show ReFOCS to outperform multiple state-of-the-art methods.

ICRB: An improved consensus‐based redactable blockchain considering contextual consistency

AbstractBlockchain is a distributed ledger proposed by Satoshi Nakamoto. It has become one of the most important networks for humans in the world. With the rapid development of blockchain technology, an increasing number of criminals are engaging in fraudulent activities, extortion and other financial crimes on the blockchain. Due to the immutability of blockchain, it is difficult for users to recover their losses. Some scholars have proposed the concept of the redactable blockchain. However, most redactable blockchain schemes fail to consider the impact of modification on the contextual economic relationships of the blockchain. We propose an improved redactable blockchain considering contextual consistency. Additionally, we utilize a security deposit mechanism to guarantee financial contextual consistency for the modified blockchain. We distribute users' security deposit account private keys to random users on the blockchain using Verifiable Secret Sharing, Verifiable Random Functions and other cryptographic primitives to achieve decentralized key management and modification. Experiments show that the performance of our scheme is acceptable in realistic situations.

Chipless RFID Tag Implementation and Machine-Learning Workflow for Robust Identification

In this work, we describe a complete step-by-step workflow to apply machine-learning (ML) classification for chipless radio-frequency identification (RFID) tag identification, covering: 1) the tag implementation criteria for circular ring resonator (CRR) and square ring resonator (SRR) arrays for ML interoperability; 2) the data collection procedure to get a sufficiently representative dataset of real measurements; 3) the ML techniques to visualize the data and reduce its dimensionality; 4) the evaluation of the ML classifier to ensure high-accuracy predictions on new measurements; and 5) a thresholding scheme to increase the certainty of the predictions. The differences in the tags’ frequency responses are maximized by optimizing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hamming distance</i> between the tag identifiers (IDs) and by controlling each resonator array’s radar cross section (RCS) level. We show that the proposed workflow achieves perfect accuracy for the identification of four tags at a fixed distance of 160 cm. We also evaluate the performance of the proposed workflow to identify up to 16 tags within a flexible range (up to 140 cm), showcasing the tradeoff between the number of tags that can be correctly classified based on the reading range.

Two (w, ω, n) weighted threshold quantum secret sharing schemes on d-level single quantum systems

Quantum secret sharing occupies an important position in quantum cryptography. In this paper, we propose two weighted threshold quantum secret sharing schemes on d-level single quantum systems that share classical information and quantum states, respectively. The dealer Alice distributes the secret share to each participant using the Chinese Remainder Theorem(CRT) for a polynomial ring, which can realize secret sharing among participants with different weights. In classical information sharing, the dealer can share multiple secrets in one round of the protocol and the participants can verify the correctness of the recovered secrets. In quantum state sharing, the dealer can share multiple single quantum states in one round of protocol. Finally, we demonstrate that our scheme is resistant to common external and internal attacks and compare it with related schemes.

Fast two-party signature for upgrading ECDSA to two-party scenario easily

ECDSA is a standardized signature scheme and is widely used in many fields. However, most two-party ECDSA require a complicated multi-party computation technique to compute the multiplication of many shared secrets and necessitate expensive zero-knowledge proofs to deal with malicious adversaries.In this paper, we propose a simple yet fast two-party signing protocol based on ECDSA security, whose signing operation of each party is similar to the original ECDSA signing algorithm. Our protocol can enjoy the same efficiency as ECDSA and upgrades the existing ECDSA application to two-party scenario friendly. For this purpose, we first introduce a variant of ECDSA called combinatorial ECDSA, which is as secure as the standard ECDSA and can be easily divided into two parts. Then, we devise a two-party signing protocol without complicated multi-party computation from the combinatorial ECDSA and give the security proof based on ECDSA. Compared with the state-of-the-art two-party ECDSA, the signing result of each party in our protocol maintains the structure of ECDSA signature, which can be verified easily and avoids expensive zero-knowledge proofs. Last, we conduct an experimental evaluation, demonstrating that the performance of our combinatorial ECDSA and two-party signature is similar to ECDSA and the experimental results show that our proposed schemes are practical.

HN3S: A Federated AutoEncoder framework for Collaborative Filtering via Hybrid Negative Sampling and Secret Sharing

Federated recommender systems can serve users with suitable item recommendations while preserving their privacy, but most current works cannot serve non-participant users. We consider that Federated AutoEncoder for Collaborative Filtering (FAE-CF) is expected to solve this problem. However, FAE-CF encounters privacy protection concerns and high communication overhead issues. In this paper, we investigate FAE-CF’s privacy protection capabilities and communication volume, unveiling its potential privacy security issues and inferior practicality. By analyzing the FAE-CF gradient, we propose an effective attack that could recover most user data, with all recovery metrics above 95.53% in our simulation experiments. To address these challenges, we propose Hybrid Negative Sampling and Secret Sharing (HN3S), a privacy-preserving method designed for FAE-CF. HN3S uses hybrid negative sampling to reduce communication overhead and secret sharing to protect user data. Experiments conducted on various public datasets with multiple AE-CFs have shown that HN3S can effectively protect the privacy of FAE-CF, reducing the communication overhead by up to 60.44%, while maintaining the performance reduction within 10.78%. Our research reveals significant privacy risks and poor utility inherent in current FAE-CFs, and provides a feasible solution to protect user privacy and balance communication overhead with algorithm performance.

End-to-end data security with DMaya on IPFS: keyless secured private swarm for the closed user group

The interplanetary File system (IPFS) provides data communication using plaintext over distributed and decentralized systems. Up till now, the security of data has mostly been established using keys. To facilitate classified data communication within a Closed User Group (CUG), services of IPFS private swarm are required to render additional content encryption. Currently, there is no inherent security built into IPFS. This paper presents an application DMaya on IPFS to provide keyless encryption on IPFS based on the Quasigroup-based Secret Sharing Scheme (QSS). IPFS is used as a storage medium, irrespective of the nature of data. Data security in decentralized mode with IPFS is provided with DMaya. The classified data of all types (i.e. video data, audio data, text data, and image data) when subjected to DMaya application, are fragmented into smaller fragments then processed by the probabilistic Quasigroup-based Secret Sharing (QSS) Scheme. DMaya keyless encryption generates the metadata tag file at the source node. The recipient node within the same private IPFS swarm, on receipt of metadata tag file reconstructs the original data with DMaya application.

NextGenV2V: Authenticated V2V communication for next generation vehicular network using (2, n)-threshold scheme

Securing end-to-end communication in Vehicular Ad-hoc Networks (VANET) is crucial due to zero trust between the communicating entities. Additionally, the underlying communication needs to be lightweight in many cases, especially when vehicles are moving at high speeds or deploying applications like Vehicular Sensor Networks (VSN) and next-generation vehicular networks with densely deployed small-cells and micro-cells. The traditional VANET facilitates two types of communication: Vehicle-to-Infrastructure (V2I) communication to establish trust between a Vehicle User (Vi) and roadside infrastructure, followed by Vehicle-to-Vehicle communication for information exchange. Unlike existing protocols that encounter security issues and high authentication delay as they require communication with a Trusted Authority (TA) every time Vi needs authentication or re-authentication, we present a symmetric key-based an authenticated V2V communication for next generation vehicular network using (2, n)-threshold scheme, called NextGenV2V. In NextGenV2V, authentication of a newly visited Vi is performed with the help of TA, but re-authentication of a pre-authenticated Vi is done without the support of TA. This significantly reduces communication overhead during re-authentication and improves the overall authentication delay. NextGenV2V is designed in such a way that at least 2 out of n registered vehicle users must cooperate to compute a shared secret key. This feature ensures improved security, as attackers cannot breach the system’s security unless two or more vehicles are compromised. Furthermore, considering that an attacker cannot physically be present in more than one location simultaneously, the system’s security remains intact even if a current vehicle is compromised. The proposed protocol is also verified to resist known security attacks, both formally and informally. To demonstrate the effectiveness of NextGenV2V, a comparative performance analysis is presented that proves its suitability for next-generation vehicular networks.

Secure One-time Password Generation using Shamir's Secret Sharing

- The process of authenticating users is critical for ensuring security. Static passwords are a popular and traditional authentication method due to their simplicity, but they are vulnerable to attacks such as password leakage, replay, guessing, eavesdropping, phishing, and spoofing, among others. One-time passwords (OTP) were introduced to address these limitations, but research has found that they are not immune to middle man attacks. To enhance the security of OTP, a new technique called Visual Cryptography Schema is proposed in this paper. In this method, we use Shamir’s Secret Sharing theory in which the main OTP is parted into three shares. Out of these shares, one is sent to the user and the other two shares are stored in different locations in the server. To retrieve

Reversible Data Hiding in Encrypted Images With Secret Sharing and Hybrid Coding

Reversible Data Hiding in Encrypted Images With Secret Sharing and Hybrid Coding

Quadratic Secret Sharing and Conditional Disclosure of Secrets

Quadratic Secret Sharing and Conditional Disclosure of Secrets

A verifiable (t,n) threshold quantum secret sharing scheme based on asymmetric binary polynomial

Threshold quantum secret sharing is a typical method for quantum secret sharing (QSS) schemes. In this paper, we propose a verifiable threshold QSS scheme based on the -dimensional Bell state and asymmetric binary polynomial. In this scheme, the dealer encodes the secret using the asymmetric binary polynomial and generates the corresponding secret share for each participant. Then, the dealer prepares the -dimensional Bell state, and the participants perform corresponding unitary operations on the particles in transmission to recover the secret. With the hash function and the session key pairs, not only the cheating behavior of the dishonest participants can be detected, but also the specific cheaters can be identified. Furthermore, we consider the case when no less than participants cooperate to recover the secret, which makes the proposed scheme more practical. Analyses show that the scheme can resist forgery attack, collusion attack and other common attacks.

Secure Two-Party Decision Tree Classification Based on Function Secret Sharing

Decision tree models are widely used for classification tasks in data mining. However, privacy becomes a significant concern when training data contain sensitive information from different parties. This paper proposes a novel framework for secure two-party decision tree classification that enables collaborative training and evaluation without leaking sensitive data. The critical techniques employed include homomorphic encryption, function secret sharing (FSS), and a custom secure comparison protocol. Homomorphic encryption allows computations on ciphertexts, enabling parties to evaluate an encrypted decision tree model jointly. FSS splits functions into secret shares to hide sensitive intermediate values. The comparison protocol leverages FSS to securely compare attribute values to node thresholds for tree traversal, reducing overhead through efficient cryptographic techniques. Our framework divides computation between two servers holding private data. A privacy-preserving protocol lets them jointly construct a decision tree classifier without revealing their respective inputs. The servers encrypt their data and exchange function secret shares to traverse the tree and obtain the classification result. Rigorous security proofs demonstrate that the protocol protects data confidentiality in a semihonest model. Experiments on benchmark datasets confirm that the approach achieves high accuracy with reasonable computation and communication costs. The techniques minimize accuracy loss and latency compared to prior protocols. Overall, the paper delivers an efficient, modular framework for practical two-party secure decision tree evaluation that advances the capability of privacy-preserving machine learning.

Adaptive decision threshold for an optical multipath-interference-impaired short-reach 50-Gbps PAM4 transmission.

The short-reach optical transmission systems based on intensity modulation and direct detection (IM/DD) are gradually evolving into the networks with complex link topologies and connections, especially inside the data center. Multipath interference (MPI) introduces irregular fluctuations in the 4-level pulse amplitude modulation (PAM4) signals and therefore affects the transmission performance. This Letter proposes an adaptive decision threshold (ADT) scheme to dynamically update the decision threshold, which can track the signal fluctuations in real time and mitigate the impact of MPI noise on the transmitted PAM4 signals. Numerical simulation results present that the proposed ADT scheme can significantly improve the bit error rate (BER) performance of the MPI-impaired PAM4 transmission system considering different MPI levels and laser linewidths. In a 50-Gbps PAM4 transmission system, the ADT can improve the MPI tolerance by more than beyond 6 dB when the BER reaches the KP4-forward error correction (FEC) criterion (2.4 × 10-4), presenting a better denoising performance than the existing MPI-mitigation algorithms A1 and A2. Moreover, the ADT scheme offers a lower computation complexity compared with A1 and A2, making it more practical for implementation.

A Lattice-Based Single-Share Secret Sharing Scheme

A Lattice-Based Single-Share Secret Sharing Scheme

Acoustic bird species classification under low SNR and small-scale dataset conditions

Bird species classification based on acoustic monitoring has attracted increasing attention. Two common methods, deep learning and feature extraction-based, are widely applied for bird sound classification. Although deep learning based methods have shown great success, they usually require an enormous amount of labeled data to train models, which could make these approaches prohibitive since collecting enough labeled samples of various species, especially rare birds, is a difficult task. On the other hand, feature extraction based methods are less dependent on the number of samples and have achieved remarkable classification performance for small-scale datasets, however they prove susceptible to signal-to-noise ratio (SNR). To address this problem, a new method is proposed for acoustic bird species classification in this work, which is well suited to low SNR and small-scale dataset conditions. Firstly, an adaptive threshold scheme based on the constant false alarm rate (CFAR) criterion is designed to more effectively detect potential bird sound segments in field recordings. Then, the local wavelet acoustic pattern (LWAP) and Mel-frequency cepstral coefficients (MFCC) features are extracted, which are further processed using improved vector of locally aggregated descriptors (VLAD) encoding. Finally, encoded LWAP and MFCC features are fused with dimensionality reduction and a classifier with weighted distance is used for classification. Corresponding feature space analysis upon feature fusion is also provided demonstrating explicit improvement in bird sounds discrimination under low SNR conditions. Experimental results on the dataset consisting of 11 bird species under low SNR and small-scale conditions reveal that the proposed method achieves considerable improvement in classification performance as compared with other feature extraction based methods. Meanwhile, our approach also outperforms state-of-the-art deep learning based methods.

Efficient Mediated Quantum Secret Sharing Protocol in a Restricted Quantum Environment

AbstractThe multiparty‐mediated quantum secret sharing (MQSS) protocol proposed by Tsai et al. [Quantum Inf. Process., 2022, 21, 63] allows n restricted users with limited quantum capabilities to share secret information using a dishonest third party with full quantum capabilities. Although the MQSS protocol allows restricted users to achieve secret sharing with lightweight quantum capabilities, the qubit efficiency of this protocol can be further improved. Therefore, this study proposes a measurement property of the graph state to design an efficient mediated quantum secret‐sharing protocol in the same quantum environment as that of Tsai et al.’s protocol. The proposed MQSS protocol not only inherits the lightweight property of Tsai et al.’s protocol but also improves the qubit efficiency of Tsai et al.’s protocol by times. Security analysis is performed to show that the proposed MQSS protocol can avoid collective, collusion, and Trojan horse attacks. Furthermore, this study uses quantum network simulation software to implement Tsai et al.’s protocol and the proposed protocol to prove the feasibility of the proposed MQSS protocol and show that it is more efficient than Tsai et al.’s protocol.