Multiple Data Owners Research Articles

Profit-driven distributed trading mechanism for IoT data

Data trading is a crucial means of unlocking the value of Internet of Things (IoT) data. However, IoT data differs from traditional material goods due to its intangible and replicable nature. This difference leads to ambiguous data rights, confusing pricing, and challenges in matching. Additionally, centralized IoT data trading platforms pose risks such as privacy leakage. To address these issues, we propose a profit-driven distributed trading mechanism for IoT data. First, a blockchain-based trading architecture for IoT data, leveraging the transparent and tamper-proof features of blockchain technology, is proposed to establish trust between data owners and data requesters. Second, an IoT data registration method that encompasses both rights confirmation and pricing is designed. The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data. For pricing, we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network. Finally, an IoT data matching method is designed based on the Stackelberg game. This establishes a Stackelberg game model involving multiple data owners and requesters, employing a hierarchical optimization method to determine the optimal purchase strategy. The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated. Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.

MemberShield: A framework for federated learning with membership privacy

Federated Learning (FL) allows multiple data owners to build high-quality deep learning models collaboratively, by sharing only model updates and keeping data on their premises. Even though FL offers privacy-by-design, it is vulnerable to membership inference attacks (MIA), where an adversary tries to determine whether a sample was included in the training data. Existing defenses against MIA cannot offer meaningful privacy protection without significantly hampering the model’s utility and causing a non-negligible training overhead. In this paper we analyze the underlying causes of the differences in the model behavior for member and non-member samples, which arise from model overfitting and facilitate MIAs. Accordingly, we propose MemberShield, a generalization-based defense method for MIAs that consists of: (i) one-time preprocessing of each client’s training data labels that transforms one-hot encoded labels to soft labels and eventually exploits them in local training, and (ii) early stopping the training when the local model’s validation accuracy does not improve on that of the global model for a number of epochs. Extensive empirical evaluations on three widely used datasets and four model architectures demonstrate that MemberShield outperforms state-of-the-art defense methods by delivering substantially better practical privacy protection against all forms of MIAs, while better preserving the target model utility. On top of that, our proposal significantly reduces training time and is straightforward to implement, by just tuning a single hyperparameter.

A Multi-Dimensional Reverse Auction Mechanism for Volatile Federated Learning in the Mobile Edge Computing Systems

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients’ volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets.

Increasing trust in AI through privacy preservation and model explainability: Federated Learning of Fuzzy Regression Trees

Federated Learning (FL) lets multiple data owners collaborate in training a global model without any violation of data privacy, which is a crucial requirement for enhancing users’ trust in Artificial Intelligence (AI) systems. Despite the significant momentum recently gained by the FL paradigm, most of the existing approaches in the field neglect another key pillar for the trustworthiness of AI systems, namely explainability. In this paper, we propose a novel approach for FL of fuzzy regression trees (FRTs), which are generally acknowledged as highly interpretable by-design models. The proposed FL procedure is designed for the scenario of horizontally partitioned data and is based on the transmission of aggregated statistics from the clients to a central server for the tree induction procedure. It is shown that the proposed approach faithfully approximates the ideal case in which the tree induction algorithm is applied on the union of all local datasets, while still ensuring privacy preservation. Furthermore, the FL approach brings benefits, in terms of generalization capability, compared to the local learning setting in which each participant learns its own FRT based only on the private, local, dataset. The adoption of linear models in the leaf nodes ensures a competitive level of performance, as assessed by an extensive experimental campaign on benchmark datasets. The analysis of the results covers both the aspects of accuracy and interpretability of FRT. Finally, we discuss the application of the proposed federated FRT to the task of Quality of Experience forecasting in an automotive case-study.

Blockchain-assisted full-session key agreement for secure data sharing in cloud computing

Data sharing in cloud computing allows multiple data owners to freely share their data resources while security and privacy issues remain inevitable challenges. As a foundation of secure communication, authenticated key agreement (AKA) scheme has been recognized as a promising approach to solve such problems. However, most existing AKA schemes are based on the cloud-based architecture, privacy and security issues will inevitably occur once the centralized authority is attacked. Besides, most previous schemes require an online registration authority for authentication, which may consume significant resources. To address these drawbacks, for secure data sharing in cloud computing, a blockchain-assisted full-session key agreement scheme is proposed. After the registration phase, the registration authority does not engage in authentication and key agreement process. By utilizing blockchain technology, a common session key between the remote user and cloud server can be negotiated, and a shared group key among multiple remote users can be negotiated without private information leakage. Formal and informal security proof demonstrated the proposed scheme is able to meet the security and privacy requirements. The detail performance evaluation shows that the proposed scheme has lower computation costs and acceptable communication overheads while superior security is ensured.

Balancing Interpretability and Performance: Optimizing Random Forest Algorithm Based on Point-to-Point Federated Learning

Federated learning is extensively applied in collaborative data scenarios involving multiple data owners. While the majority of state-of-the-art federated learning algorithms are currently black-box models, making it challenging for users to comprehend how decisions are made. Random forest models are extensively utilized in medical contexts owing to their exceptional interpretability. However, when faced with multicenter data, the heterogeneity of data from each center often leads to its predictive performance falling short of expectations. To mitigate this challenge, the present study introduces DFLRF (Decentralized Federated Learning Random Forest), a federated learning algorithm based on random forests. Expanding on conventional random forests, DFLRF employs federated learning to disseminate decision tree models. It assesses and consolidates tree models from all client sites, thereby comprehensively addressing data disparities across various centers. The algorithm selects the optimal decision tree model based on the magnitude of model loss to guarantee the predictive performance of the final federated random forest model. The algorithm undergoes testing on a public dataset. Experimental results demonstrate that, compared to baseline algorithms, DFLRF enhances the AUC by 1.5% and the recall rate by 6%, while also ensuring superior interpretability.

Multiple Keyword Search in Cloud Data

Cloud computing is often regarded as a great innovation due to the increasing demand for endless storage space and reliable retrieval services. Several works have been developed on ranked multi-keyword search over cloud data with numerous data owners concerned about privacy. However, most of these methods can be broken by a clever attacker using a combination of a keyword guess and an equivalency test. Also, when searching across data from many data owners, they can't reliably return the top k results to the users. The unreliability of search results and the potential for the exposure of critical keyword information are two obvious drawbacks. To address this issue, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, allowing the cloud server to perform multikeyword search over the cloud data and then return the top-k ranked search results to data users without leaking any keyword and trapdoor information. Furthermore, we demonstrate through stringent security analysis that our method is safe from both internal and external threats. Finally, the results of the performance evaluation show that our scheme outperforms other ranked multi-keyword search systems. Keywords: cloud computing, multi-keyword retrieval, multiple data owner model, privacy sensitive, ranked.

FedCAE: A New Federated Learning Framework for Edge-Cloud Collaboration Based Machine Fault Diagnosis

With the coming of the industrial big data era, data-driven fault diagnosis models emerge recently and show potential results in many studies. However, it is impractical to collect massive high-quality data in actual industrial applications, which strongly reduces the performance of fault diagnosis models. Therefore, multiple data owners are motivated to share their own data for training a brilliant model with the aggregated dataset. Notwithstanding, it is unrealistic to share data due to the potential conflict of interest. For addressing the issue of data island, a new federated framework namely FedCAE is proposed for fault diagnosis. In this framework, each client is equipped with a convolutional autoencoder (CAE) trained by its corresponding local data. Then, all CAEs are uploaded to a server and aggregated to a global CAE according to an adaptive weighted selection mechanism. Afterwards, the global CAE is downloaded to all clients for extracting low-level features from local datasets by its encoder and uploading these features to train a global fault diagnosis classifier in the server. At last, the classifier is downloaded to all clients for completing their own diagnosis tasks. Experiment results on two bearing datasets suggest FedCAE offers a promising solution on confidential decentralized learning.

Collaborative Learning across Heterogeneous Systems with Pre-Trained Models

The increasingly decentralized and private nature of data in our digital society has motivated the development of personalized, collaborative intelligent systems that enable knowledge aggregation across multiple data owners while accommodating for their data privacy and system constraints. However, collaborative learning has only been investigated in simple and limited settings: isolated task scenarios where learning begins from scratch and does not build on prior expertise; learned model is represented in task-specific forms which are not generalizable to unseen, emerging scenarios; and more often, a universal model representation is assumed across collaborators, ignoring their local compute constraints or input representations. This restricts its practicality in continual learning scenarios with limited task data, which demand continuous adaptation and knowledge transfer across different information silos, tasks, and learning models, as well as the utilization of prior solution expertises. To overcome these limitations, my research has been focused on developing effective and scalable resource-aware collaborative learning frameworks across heterogeneous systems.

An Efficient Privacy-Preserving Ranked Multi-Keyword Retrieval for Multiple Data Owners in Outsourced Cloud

An Efficient Privacy-Preserving Ranked Multi-Keyword Retrieval for Multiple Data Owners in Outsourced Cloud

Towards blockchain-enabled decentralized and secure federated learning

The conventional machine learning process typically operates under the premise of centralized data aggregation, where all data is collected at a central location for model training. However, this raises substantial privacy concerns when the data contains private information. In this context, federated learning has emerged as a prominent solution for preserving privacy in machine learning. This innovative paradigm allows multiple data owners, or clients, to collaboratively train a machine learning model while keeping their local data unshared. A federated learning task is typically initiated by companies, often referred to as model owners, who do not possess enough training data and are willing to financially remunerate clients who contribute to the development of the federated learning model. This situation demands a trading platform that enables model owners to effectively select clients, while ensuring robustness against malicious clients who execute poisoning attacks for unfair financial gain. To address these issues, we design a contribution-based exploration-exploitation mechanism implemented as a smart contract. This mechanism cherry-picks clients with high data quality based on the Shapley value, which is calculated based on local models to evaluate the contribution of each client. Unlike other state-of-the-art security mechanisms, the proposed mechanism can adapt to various scenarios with heterogeneous data distribution and various attacks, while mitigating the effect of malicious behaviors without compromising training accuracy. To accelerate the time-consuming calculation of the Shapley value, we design a parallel computing algorithm that partitions blockchain nodes into multiple shards and distributes calculation tasks among them. The algorithm improves efficiency and tolerates potential false calculation results from malicious nodes.

Blockchain-cloud privacy-enhanced distributed industrial data trading based on verifiable credentials

Industrial data trading can considerably enhance the economic and social value of abundant data resources. However, traditional data trading models are plagued by critical flaws in fairness, security, privacy and regulation. To tackle the above issues, we first proposed a distributed industrial data trading architecture based on blockchain and cloud for multiple data owners. Subsequently, we realized implemented distributed identity management by the distributed verifiable credentials scheme that possesses the desirable properties, i.e., selective disclosure, multi-show unlinkability, threshold traceability, and public verifiability. Finally, we presented a fair trading mechanism without trusted third parties based on smart contracts, and we employed blockchain and multi-signature to ensure data integrity during data storage and trading. The security and performance analysis shows that our proposal is feasible for sensitive data trading for multiple data owners and provides a useful exploration for future industrial data trading and management.

Privacy Preserving Feature Selection for Sparse Linear Regression

Privacy-Preserving Machine Learning (PPML) provides protocols for learning and statistical analysis of data that may be distributed amongst multiple data owners (e.g., hospitals that own proprietary healthcare data), while preserving data privacy. The PPML literature includes protocols for various learning methods, including ridge regression. Ridge regression controls the L2 norm of the model, but does not aim to strictly reduce the number of non-zero coefficients, namely the L0 norm of the model. Reducing the number of non-zero coefficients (a form of feature selection) is important for avoiding overfitting, and for reducing the cost of using learnt models in practice. In this work, we develop a first privacy-preserving protocol for sparse linear regression under L0 constraints. The protocol addresses data contributed by several data owners (e.g., hospitals). Our protocol outsources the bulk of the computation to two non-colluding servers, using homomorphic encryption as a central tool. We provide a rigorous security proof for our protocol, where security is against semi-honest adversaries controlling any number of data owners and at most one server. We implemented our protocol, and evaluated performance with nearly a million samples and up to 40 features.

TrustDFL: A Blockchain-Based Verifiable and Trusty Decentralized Federated Learning Framework

Federated learning is a privacy-preserving machine learning framework where multiple data owners collaborate to train a global model under the orchestra of a central server. The local training results from trainers should be submitted to the central server for model aggregation and update. Busy central server and malicious trainers can introduce the issues of a single point of failure and model poisoning attacks. To address the above issues, the trusty decentralized federated learning (called TrustDFL) framework has been proposed in this paper based on the zero-knowledge proof scheme, blockchain, and smart contracts, which provides enhanced security and higher efficiency for model aggregation. Specifically, Groth 16 is applied to generate the proof for the local model training, including the forward and backward propagation processes. The proofs are attached as the payloads to the transactions, which are broadcast into the blockchain network and executed by the miners. With the support of smart contracts, the contributions of the trainers could be verified automatically under the economic incentive, where the blockchain records all exchanged data as the trust anchor in multi-party scenarios. In addition, IPFS (InterPlanetary File System) is introduced to alleviate the storage and communication overhead brought by local and global models. The theoretical analysis and estimation results show that the TrustDFL efficiently avoids model poisoning attacks without leaking the local secrets, ensuring the global model’s accuracy to be trained.

An efficient multi-data owner cooperative resource sharing scheme against key regeneration in edge computing

In cloud storage, attribute-based keyword searchable encryption realizes multi-user fine-grained authorization access control and outsourcing data sharing. However, (1) resisting key regeneration is an urgent problem to be solved in practical applications of attribute-based keyword search; (2) the general attribute-based keyword search scheme ignores the application scenario where multiple data owners jointly manage files. In addition, the interaction of data between intelligent terminals and cloud servers has become more frequent, leading to issues such as central load, transmission efficiency, and data privacy protection in cloud computing. To address the above issues, we propose an attribute-based searchable encryption scheme with the multi-data owner based on edge computing. In this scheme, the Pedersen (k,n) secret sharing protocol and the Reciprocal protocol are used to realize the common management and sharing of data by the multi-data owner to enhance data security. Secondly, the attribute set and identity information of each user are embedded into the private key to prevent key regeneration and resist user collusion attacks. Thirdly, edge computing is introduced, and part of the storage and decryption work is outsourced to edge nodes to reduce the computing load of users and data transmission bandwidth. Security proof and performance analysis show that the proposed scheme has strong security and practicability in multi-user sharing scenarios such as Online Education with multi-data owners.

Ensuring Data Integrity And Security In Diverse Cloud Environments To Prevent Duplicacy.

Data deduplication is a valuable technique for compressing and minimizing data duplication during data transfers, especially in cloud environments. By eliminating redundant data, it optimizes transmission capacity and reduces memory usage. To ensure the integrity of sensitive data, encryption is applied throughout the deduplication process. The SHA algorithm is commonly used for storing text data during deduplication. It generates security bits by padding the text and computes a hash consisting of hexadecimal, string, and integer data. Hash-based deduplication involves hashing the entire file and treating the hash values of text data as unique identifiers. This allows clients to identify duplicate data within the cloud. The Proof-of-Work (PoW) algorithm is widely utilized in blockchain networks like Bitcoin and Ethereum. Its primary function is to verify transactions through a process called mining. Miners engage in competition to solve complex mathematical problems, and the first one to find a solution is granted the right to add a new block to the blockchain. PoW relies on cryptographic hashing, such as the SHA-256 hashing function, to validate and secure transactions. On the other hand, the Proof of Retrievability (PoR) algorithm finds application in cloud computing systems. It serves as a consensus mechanism, ensuring that cloud storage providers store and retrieve data accurately. PoR enables cloud providers to demonstrate to consumers that their files can be fully recovered. This algorithm incorporates cryptographic proofs that validate the integrity and availability of the stored data. In cloud storage, deduplication is often implemented using the Memory mapping technique (MPT). This allows multiple data owners to store the same data in a single copy, enhancing storage efficiency. To maintain data security, encryption is applied both before and during the deduplication process, ensuring that sensitive information remains protected. In summary, data deduplication is a powerful method for compressing data, minimizing duplication, and optimizing transmission capacity. With encryption techniques and hash-based identification, it provides secure and efficient deduplication in cloud environments, while memory deduplication and MPT support further enhance performance and storage efficiency.

End-to-end Privacy Preserving Training and Inference for Air Pollution Forecasting with Data from Rival Fleets

Privacy-preserving machine learning (PPML) promises to train machine learning (ML) models by combining data spread across multiple data silos. Theoretically, secure multiparty computation (MPC) allows multiple data owners to train models on their joint data without revealing the data to each other. However, the prior implementations of this secure training using MPC have three limitations: they have only been evaluated on CNNs, and LSTMs have been ignored; fixed point approximations have affected training accuracies compared to training in floating point; and due to significant latency overheads of secure training via MPC, its relevance for practical tasks with streaming data remains unclear. The motivation of this work is to report our experience of addressing the practical problem of secure training and inference of models for urban sensing problems, e.g., traffic congestion estimation, or air pollution monitoring in large cities, where data can be contributed by rival fleet companies while balancing the privacy-accuracy trade-offs using MPC-based techniques.Our first contribution is to design a custom ML model for this task that can be efficiently trained with MPC within a desirable latency. In particular, we design a GCN-LSTM and securely train it on time-series sensor data for accurate forecasting, within 7 minutes per epoch. As our second contribution, we build an end-to-end system of private training and inference that provably matches the training accuracy of cleartext ML training. This work is the first to securely train a model with LSTM cells. Third, this trained model is kept secret-shared between the fleet companies and allows clients to make sensitive queries to this model while carefully handling potentially invalid queries. Our custom protocols allow clients to query predictions from privately trained models in milliseconds, all the while maintaining accuracy and cryptographic security.

Searchable encryption algorithm based on key aggregation of multiple data owners in data sharing

Searchable encryption allows data owners to outsource their encrypted data to cloud servers and provide a searchable encryption service without exposing their sensitive information. Existing searchable encryption schemes provide the service for users to query the encrypted data of a single data owner. However, these schemes ignore the problem of query matching efficiency when users query the encrypted data of multiple data owners simultaneously. Specifically, for the same content encrypted by different data owners, users need to generate different query trapdoors in the query phase and match the different query trapdoors and indexes in the matching phase. In this paper, we propose a searchable encryption algorithm based on the key aggregation of multiple data owners. The algorithm first generates keys for multiple data owners using the mutual inversion of elements in a finite field to ensure the consistency of ciphertext processing. Then, it generates index keys to build encrypted indexes and ensure the searchability of the ciphertext. Finally, it generates query trapdoors based on multi-key aggregation to match the encrypted indexes of multiple data owners. The theoretical analysis and simulation results show that the proposed algorithm improves the query efficiency and protects the query indexes and trapdoors.

Optimal and Efficient Searchable Encryption with Single Trapdoor for Multi-Owner Data Sharing in Federated Cloud Computing

Cloud computing, an Internet based computing model, has changed the way of data owners store and manage data. In such environment, data sharing is very important with more efficient data access control. Issuing an aggregate key to users on data enables and authorizes them to search for data of select encrypted files using trapdoor or encrypted keyword. The existing schemes defined for this purpose do have certain limitations. For instance, Cui et al. scheme is elegant but lacks in flexibility in access control in presence of multiple data owners sharing data to users. Its single trapdoor approach needs transformation into individual trapdoors to access data of specific data owner. Moreover, the existing schemes including that of Cui et al. does not support federated cloud. In this paper we proposed an efficient key aggregate searchable encryption scheme which enables multiple featuressuch as support for truly single aggregate key to access data of many data owners, federated cloud support,query privacy, controlled search process and security against cross-pairing attack. It has algorithms for setup, keygen, encrypt, extract, aggregate, trapdoor, test and federator. In multi-user setting it is designed to serve data owners and users with secure data sharing through key aggregate searchable encryption The proposed scheme supports federated cloud. Experimental results revealed that the proposed scheme is provably secure withrelatively less computational overhead and time complexity when compared with the state of the art.

Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System

Federated learning (FL) enables multiple data owners to collaboratively train machine learning (ML) models without disclosing their raw data. In the vertical federated learning (VFL) setting, the collaborating parties have data from the same set of users but with disjoint attributes. After constructing the VFL models, the parties deploy the models in production systems to infer prediction requests. In practice, the prediction output itself may not be convincing for party users to make the decisions, especially in high-stakes applications. Model interpretability is therefore essential to provide meaningful insights and better comprehension on the prediction output. In this paper, we propose Falcon, a novel privacy-preserving and interpretable VFL system. First, Falcon supports VFL training and prediction with strong and efficient privacy protection for a wide range of ML models, including linear regression, logistic regression, and multi-layer perceptron. The protection is achieved by a hybrid strategy of threshold partially homomorphic encryption (PHE) and additive secret sharing scheme (SSS), ensuring no intermediate information disclosure. Second, Falcon facilitates understanding of VFL model predictions by a flexible and privacy-preserving interpretability framework, which enables the implementation of state-of-the-art interpretable methods in a decentralized setting. Third, Falcon supports efficient data parallelism of VFL tasks and optimizes the parallelism factors to reduce the overall execution time. Falcon is fully implemented, and on which, we conduct extensive experiments using six real-world and multiple synthetic datasets. The results demonstrate that Falcon achieves comparable accuracy to non-private algorithms and outperforms three secure baselines in terms of efficiency.