Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretation and efficiency of training. However, there is a fundamental lack of research on how to conduct VFL securely over distributed labels. This work is the first to fill this gap by designing a novel protocol, called FEVERLESS, based on XGBoost. FEVERLESS leverages secure aggregation via information masking technique and global differential privacy provided by a fairly and randomly selected noise leader to prevent private information from being leaked in the training process. Furthermore, it provides label and data privacy against honest-but-curious adversaries even in the case of collusion of <inline-formula><tex-math notation="LaTeX">$n - 2$</tex-math></inline-formula> out of n clients. We present a comprehensive security and efficiency analysis for our design, and the empirical results from our experiments demonstrate that FEVERLESS is fast and secure. In particular, it outperforms the solution based on additive homomorphic encryption in runtime cost and provides better accuracy than the local differential privacy approach.

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Federated Learning (FL) is a beneficial decentralized learning approach for preserving the privacy of local datasets of distributed agents. However, the distributed property of FL and untrustworthy data introducing the vulnerability to backdoor attacks. In this attack scenario, an adversary manipulates its local data with a specific trigger and trains a malicious local model to implant the backdoor. During inference, the global model would misbehave for any input with the trigger to the attacker-chosen prediction. Most existing backdoor attacks against FL focus on bypassing defense mechanisms, without considering the inspection of model parameters on the server. These attacks are susceptible to detection through dynamic clustering based on model parameter similarity. Besides, current methods provide limited imperceptibility of their trigger in the spatial domain. To address these limitations, we propose a stealthy backdoor attack called "Chironex"against FL with an imperceptible trigger in frequency space to deliver attack effectiveness, stealthiness and robustness against various countermeasures on FL. We first design a frequency trigger function to generate an imperceptible frequency trigger to evade human inspection. Then we fully exploit the attacker's advantage to enhance attack robustness by estimating benign updates and analyzing the impact of the backdoor on model parameters through a task-sensitive neuron searcher. It disguises malicious updates as benign ones by reducing the impact of backdoor neurons that greatly contribute to the backdoor task based on activation value, and encouraging them to update towards benign model parameters trained by the attacker. We conduct extensive experiments on various image classifiers with real-world datasets to provide empirical evidence that Chironex can evade the most recent robust FL aggregation algorithms, and further achieve a distinctly higher attack success rate than existing attacks, without undermining the utility of the global model.

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Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing systems, however, are only robust when most of the clients are honest. FLTrust (NDSS '21) and Zeno++ (ICML '20) do not make such an honest majority assumption but can only be applied to scenarios where the server is provided with an auxiliary dataset used to filter malicious updates. FLAME (USENIX '22) and EIFFeL (CCS '22) maintain the semi-honest majority assumption to guarantee robustness and the confidentiality of updates. It is therefore currently impossible to ensure Byzantine robustness and confidentiality of updates without assuming a semi-honest majority. To tackle this problem, we propose a novel Byzantine-robust and privacy-preserving FL system, called MUDGUARD, to capture malicious minority and majority for server and client sides, respectively. Our experimental results demonstrate that the accuracy of MUDGUARD is practically close to the FL baseline using FedAvg without attacks (approximate 0.8% gap on average). Meanwhile, the attack success rate is around 0%-5% even under an adaptive attack tailored to MUDGUARD. We further optimize our design by using binary secret sharing and polynomial transformation leading to communication overhead and runtime decreases of 67%-89.17% and 66.05%-68.75%, respectively.

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Searchable symmetric encryption has been vulnerable to inference attacks that rely on uniqueness in leakage patterns. However, many keywords in datasets lack distinctive leakage patterns, limiting the effectiveness of such attacks. The file injection attacks, initially proposed by Cash et al. (CCS 2015), have shown impressive performance with 100% accuracy and no prior knowledge requirement. Nevertheless, this attack fails to recover queries with underlying keywords not present in the injected files. To address these limitations, our research introduces a novel attack strategy called LEAP-Hierarchical Fusion Attack (LHFA) that combines the strengths of both file injection attacks and inference attacks. Before initiating keyword injection, we introduce a new approach for inert/active keyword selection. In the phase of selecting injected keywords, we focus on keywords without unique leakage patterns and recover them, leveraging their presence for document recovery. Our goal is to achieve an amplified effect in query recovery. We demonstrate a minimum query recovery rate of 1.3 queries per injected keyword with a 10% data leakage of a real-life dataset, and initiate further research to overcome challenges associated with non-distinctive keywords.

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Synthetic data generation plays a crucial role in many areas where data is scarce and privacy/confidentiality is a significant concern. Generative Adversarial Networks (GANs), arguably one of the most widely used data synthesis techniques, allow for the training of a model (i.e., generator) that can generate real-looking data by playing a min-max game with a discriminator model. When multiple organizations are reluctant to share their sensitive data, GANs models can be trained in a federated manner, commonly with the use of differential privacy (DP). In order to achieve a reasonable level of model utility, DP trades privacy exhibiting vulnerability to various attacks (e.g., membership inference attack). In this paper, we propose a hybrid solution, PP-FedGAN, to the asynchronous federated, privacy-preserving training of GANs models by combining the CKKS homomorphic encryption (HE) scheme with differential privacy. The addition of HE results in around 10 seconds of overhead on the client side per round and 115 seconds on the entire training procedure. We also analyze the security of PP-FedGAN under the honest-but-curious security model. Where stronger security guarantees are required, our proposal presents a better alternative to solutions that only employ DP.

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Existing proxy re-encryption (PRE) schemes to secure cloud data sharing raise challenges such as supporting the heterogeneous system efficiently and achieving the unbounded feature. To address this problem, we proposed a fast and secure unbounded cross-domain proxy re-encryption scheme, named FABRIC, which enables the delegator to authorize the semi-trusted cloud server to convert one ciphertext of an identity-based encryption (IBE) scheme to another ciphertext of an attribute-based encryption (ABE) scheme. As the first scheme to achieve the feature mentioned above, FABRIC not only enjoys constant computation overhead in the encryption, decryption, and re-encryption phases when the quantity of attributes increases, but is also unbounded such that the new attributes or roles could be adopted into the system anytime. Furthermore, FABRIC achieves adaptive security under the decisional linear assumption (DLIN). Eventually, detailed theoretical and experimental analysis proved that FABRIC enjoys excellent performance in efficiency and practicality in the cloud computing scenario.

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Password hardening encryption (PHE) is an emerging primitive in recent years. It can resist offline attack brought by keyword guessing attack from server via adding a third party with crypto services joining the decryption process. This primitive enhances the password authentication protocol and adds encryption functionality. This paper presents an active attack from server in the first scheme that introduced this primitive. This attack combines the idea from a cutting-edge threat called algorithm substitution attack which is undetectable and makes the server capable of launching offline attack. This result shows that the original PHE scheme can not resist attacks from malicious server. Then this study tries to summarize the property that an algorithm substitution attack resistant scheme should have. After that this paper presents a PHE scheme that can resist such kind of attacks from malicious server with simulation results. Finally, this study concludes the result and gives some expectation for future systematic research on interactive protocols under algorithm substitution attack.

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It has become a trend for clients to outsource their encrypted databases to remote servers and then leverage the Searchable Encryption technique to perform secure data retrieval. However, the method has yet to be considered a crucial need for replication on searchable encrypted data. It calls for challenging works on Dynamic Searchable Symmetric Encryption (DSSE) since clients must share the search capability of the encrypted data replicas and guarantee forward and backward privacy. We define a new notion called 'Keyword Search Shareable Encryption' (KS2E2E) and the corresponding security model capturing forward and backward privacy. In our notion, data owners are allowed to share search indexes of the encrypted data with users. A search index will be updated with a new search key before sharing to guarantee the data privacy of the source database. The target database also inherits data search efficiency along with the shared data. We further construct an instance of KS2E called Branch, prove its security, and use real-world datasets to evaluate Branch. The evaluation results show that Branch's performance is comparable to classical DSSE schemes on search efficiency and demonstrate the effectiveness of searching encrypted data replicas from multiple owners.

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This work aims to provide a more secure access control in Hyperledger Fabric blockchain by combining multiple ID’s, attributes, and policies with the components that regulate access control. The access control system currently used by Hyperledger Fabric is first completely analyzed. Next, a new implementation is proposed that builds upon the existing solution but provides users and developers with easier ways to make access control decisions based on combinations of multiple ID’s, attributes, and policies. Our proposed implementation encapsulates the Fabric CA client to facilitate attribute addition and simplify the process of registering and enrolling a newly created certificate (corresponding to a new user). This research, concludes that it is possible to combine multiple ID’s, attributes, and policies with the help of Hyperledger Fabric’s smart contract technology. Furthermore, it could be seen that the performance impact for real-world applications is negligible compared to the insecure case of always providing access to a resource without performing access control.

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Traffic encrypted technology enables Internet users to protect their data secrecy, but it also brings a challenge to malicious package detection. To tackle this issue, researchers have investigated into encrypted traffic analysis (ETA) in recent years. Existing works, however, only focus on the accuracy of malicious flow identification. Using ETA as a technical black box, they pay little attention to the internal details and explanation of models. In this paper, we, for the first time, introduce interpretable machine learning into ETA. We aim to provide a reasonable explanation for detection results, so as to enable one to understand and further trust network security analysts. We develop a complete analysis framework, named DEV-ETA (detection, explanation and verification of ETA). DEV-ETA applies post hoc interpretation methods to explain the detection results and verify the explanation using the joint distribution of support features on the dataset. We run thorough experiments to explain the detection result using three popular explanation approaches, namely SHAP, LIME and MSS, and we verify the explanation via the feature distribution plot. The experimental results show that our design can interpret the detection result of ETA model instead of just simply treating the model as a black box.

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Modeling password distributions is a fundamental problem in password security, benefiting the research and applications on password guessing, password strength meters, honey password vaults, etc. As one of the best segment-based password models, WordPCFG has been proposed to capture individual semantic segments (called words) in passwords. However, we find WordPCFG does not address well the ambiguity of password segmentation by maximum matching, leading to the unreasonable segmentation of many password and further the inaccuracy of modeling password distributions. To address the ambiguity, we improve WordPCFG by maximum probability segmentation with A*-like pruning algorithm. The experimental results show that the improved WordPCFG cracks 99.26%–99.95% passwords, with nearly 5.67%–18.01% improvement.@en

The increasing popularity of remote Cloud File Sharing (CFS) has become a major concern for privacy breach of sensitive data. Aiming at this concern, we present a new resource sharing framework by integrating enterprise-side Attribute-Based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model, client-side Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, and cloud-side CFS service. Moreover, the framework workflow is provided to support the encrypted-file writing and reading algorithms in accordance with ABAC/XACML-based access policy and attribute credentials. However, an actual problem of realizing this framework is that policy matrix, derived from access policy, seriously affects the performance of existing CP-ABE from Lattice (CP-ABE-L) schemes. To end it, we present an optimal generation algorithm of Small Policy Matrix (SPM), which only consists of small elements, and generates an all-one reconstruction vector. Based on such a matrix, the improved CP-ABE-L scheme is proposed to reduce the cumulative errors to the minimum and prevent the enlargement of error bounds. Furthermore, we give the optimal estimation of system parameters to implement a valid Error Proportion Allocation (EPA). Our experimental results indicate that our scheme has short size of parameters and enjoys efficient computation and storage overloads. Thus, our new framework with optimization methods is conducive to enhancing the security and efficiency of remote work on CFS.

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This paper offers a prototype of a Hyperledger Fabric-IPFS based network architecture including a smart contract based encryption scheme that meant to improve the security of user’s data that is being uploaded to the distributed ledger. A new extension to the self-encryption scheme was deployed by integrating data owner’s identity into the encryption process. Such integration allows to permanently preserve ownership of the original file and link it to the person/entity who originally uploaded it. Moreover, self-encryption provides strong security guarantees that decryption of a file is computationally not feasible under the condition that the encrypted file and the key are safely stored.

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Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this article, we propose a secure vertical FL framework, named privacy-preserving vertical federated learning system over distributed labels (PIVODL), to train GBDTs with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible. Impact Statement - Federated learning is a distributed machine learning framework proposed for privacy preservation. Most federated learning algorithms work on horizontally partitioned data, with only a few exceptions considering vertically partitioned data that is widely seen in the real world. However, existing vertical federated learning makes an unrealistic assumption that data labels are distributed on only one device and no research has been reported so far that considers data labels distributed on multiple client devices. The PIVODL framework reported in this article allows us to build a secure vertical federated XGBoost system, in which the labels may distributed either on one device or on multiple devices, making it possible to apply federated learning to a wider range of real-world problems.

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Genotype imputation estimates missing genotypes from the haplotype or genotype reference panel in individual genetic sequences, which boosts the potential of genome-wide association and is essential in genetic data analysis. However, the genetic sequences involve people's privacy, confirming an individual's identification and even disease information. This work proposes a secure genotype imputation model, which uses a linear regression model and the homomorphic encryption scheme over ciphertext to impute missing genotypes. The inference model is trained with float plaintext parameters, which are round into integers to avoid high complexity homomorphic evaluation on float number operations without bootstrapping operations. Even though the rounding parameters in the inference model are not the same as those in the trained model, We find that it will no effect on the outcome of the homomorphic prediction. Thus, a high-efficiency genotype imputation inference model over the ciphertext is obtained while keeping the high-security level. The simulation results indicate that the accuracy of the secure inference model is almost the same as the original model trained on float parameters. The secure inference model's accuracy is 98.6% for a single genotype.

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Password-only authentication is one of the most popular secure mechanisms for real-world online applications. But it easily suffers from a practical threat - password leakage, incurred by external and internal attackers. The external attacker may compromise the password file stored on the authentication server, and the insider may deliberately steal the passwords or inadvertently leak the passwords. So far, there are two main techniques to address the leakage: Augmented password-authentication key exchange (aPAKE) against insiders and honeyword technique for external attackers. But none of them can resist both attacks. To fill the gap, we propose the notion of <italic>honey PAKE (HPAKE)</italic> that allows the authentication server to detect the password leakage and achieve the security beyond the traditional bound of aPAKE. Further, we build an HPAKE construction on the top of the honeyword mechanism, honey encryption, and OPAQUE which is a standardized aPAKE. We formally analyze the security of our design, achieving the insider resistance and the password breach detection. We implement our design and deploy it in the real environment. The experimental results show that our protocol only costs 71.27 ms for one complete run, within 20.67 ms on computation and 50.6 ms on communication. This means our design is secure and practical for real-world applications.

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Sidechains have been widely used to improve the interoperability and scalability of blockchain systems. Despite several interesting sidechain constructions have been proposed in the literature, they suffer from the following downsides: (1) their designs do not easily support pluggable consensus mechanisms, and (2) their communication and storage costs for cross-chain operations are not yet optimized. In this work, we first propose Ge-Co, a generic sidechain construction to realize secure asset transfers between blockchains, supporting different consensus algorithms, such as Proof-of-Stake (PoS) and Proof-of-Work (PoW). Our design is built on top of the proposed voting committee selection approach and threshold signature schemes (TSS) and meanwhile, it achieves optimally succinct and constant proof size, only yielding lightweight communication and storage costs. Ge-Co works in the semi-adaptive corruption model. To provide stronger security, we further propose PoS-Co, a PoS-based sidechain construction in the fully-adaptive corruption model. PoS-Co is based on the proposed anonymous committee selection approach, and preserves optimally succinct proof. We also formally prove that Ge-Co can achieve the security properties of atomicity and timeliness. Finally, we develop a proof-of-concept (PoC) implementation for Ge-Co, and the results demonstrate that the design is efficient and practical.@en

Data island effectively blocks the practical application of machine learning. To meet this challenge, a new framework known as federated learning was created. It allows model training on a large amount of scattered data owned by different data providers. This article presents a parallel solution for computing logistic regression based on distributed asynchronous task framework. Compared to the existing work, our proposed solution does not rely on any third-party coordinator, and hence has better security and can solve the multitraining problem. The logistic regression based on homomorphic encryption is implemented in Python, which is used for vertical federated learning and prediction of the resulting model. We evaluate the proposed solution using the MNIST dataset, and the experimental results show that good performance is achieved.

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