This paper presents Hydra, a dynamic BFT protocol that allows replicas to join and leave the system dynamically. It addresses the limitations of traditional static BFTs in managing membership changes and can be used to simplify the implementation of many features in modern blockchain applications. Hydra relies on weaker assumptions to achieve standard properties compared to the existing solution Dyno and introduces a configuration auto-transition protocol to ensure liveness. Through temporary configurations and explicitly defined replica responsibilities for request handling, Hydra pipelines membership requests alongside regular requests and realizes clarity, achieving a more efficient and smoother configuration transitions. It also employs a non-blocking configuration discovery mechanism, enabling new replicas to participate in consensus quickly. We formally prove Hydra's correctness under the dynamic BFT model. Experimental results demonstrate Hydra's ability to maintain throughput fluctuations within 5% during various replica join and leave scenarios, outperforming Dyno and existing BFT system supporting reconfiguration in both stability and efficiency. Hydra effectively manages scenarios that Dyno circumvents with stronger assumptions and quickly restores throughput to normal levels.

Local Differential Privacy (LDP) enables privacy-preserving data analytics without requiring a trusted aggregator and has attracted significant attention from both academia and industry. For key–value data, PrivKV has been proposed to support frequency and mean estimation under LDP. In PrivKV, the user first samples a key uniformly at random and applies a randomization mechanism to perturb the corresponding value. However, since both Sample and Perturb steps are conducted locally, PrivKV is susceptible to output poisoning attacks, where malicious users bypass these steps and submit crafted data, making the aggregation result biased. To address this vulnerability, we propose VPrivKV, a verifiable LDP protocol designed to defend against output poisoning attacks. VPrivKV enables users and the aggregator to jointly perform the sampling step using a coin-flipping protocol, while the perturbation is enforced through an interactive and verifiable mechanism. Furthermore, we propose an enhanced version of VPrivKV that integrates zero-knowledge proofs to prevent the adversary from forging the discretized value to suppress non-target keys, thereby further enhancing robustness. We theoretically analyze the privacy and robustness of the proposed protocols and conduct numerical simulations to demonstrate their effectiveness in defending against output poisoning attacks.

This paper seeks to enhance the performance of large-scale leader-based Byzantine Fault Tolerant (BFT) systems by proposing a novel Group BFT scheme. The scheme utilizes a two-round message transmission process to distribute the load from a single leader across multiple replicas by dividing the entire consensus network into groups, each with an equal number of replicas. Each group has a leader to process the group's voting messages into a single aggregated voting message during the first round, which is then transmitted to the consensus leader in the second round (similar process for proposing). We establish a formal system framework for Group BFT protocols with a versatile set of base components and explicit definitions, addressing the challenges inherent in designing such a system. We further design and implement two highly efficient Group BFT protocols: one that supports inter-group member exchange and the other one that does not. We theoretically prove the safety, liveness, and responsiveness of the Group BFT protocols. We conduct a formal analysis of Group BFTs' tolerance and complexity. Experimental results show that the two Group BFT protocols significantly alleviate the processing bottlenecks of the leader and highly improve throughput in large-scale systems.

Vertical Federated Learning(VFL) has gained widespread attention due to its ability of enabling collaborative model training among participants with diverse data features.Differential Privacy(DP) offers provable privacy guarantees for VFL, but existing DP-based methods typically compromise accuracy for privacy protection. To address this issue, we propose a novel scheme, called Adaptive Differential Privacy-based Vertical Federated Learning (Ada-VFed), that enhances privacy of data features and labels by adding Gaussian noise separately to the transmitted intermediate results and gradients. To improve model accuracy, we incorporate adaptive constraints through regularization terms in the objective function to mitigate the impact of clipping operations. In addition, we propose a dynamic noise injection mechanism that adjusts noise according to the importance of each dimension, thereby balancing privacy protection and model accuracy. Our theoretical analysis provides privacy guarantees and convergence insights. Extensive experiments demonstrated that our scheme significantly outperforms state-of-the-art DP-based VFL methods in terms of accuracy. Even with a small privacy budget (e.g.,ϵ = 0.5), our method improves the accuracy on MNIST, FashionMNIST, and CIFAR-10 by 13.01%, 10.08%, and 3.40%, respectively, compared to traditional DP-based VFL methods.

As the leading privacy coin, Monero is widely recognized for its high level of anonymity. Monero utilizes linkable ring signature to hide the sender of a transaction. Although the anonymity is preferred by users, it poses challenges for authorities seeking to regulate financial activities. Researchers are actively engaged in studying methods to de-anonymize Monero. Previous methods usually relied on a specific type of ring called zero-mixin ring. However, these methods have become ineffective after Monero enforced the minimum ringsize. In this paper, we propose a novel approach based on maximum weighted matching to de-anonymize Monero. The proposed approach does not rely on the existence of zero-mixin rings. Specifically, we construct a weighted bipartite graph to represent the relationship between rings and transaction outputs. Based on the empirical probability distribution derived from users’ spending patterns, three weighting methods are proposed. Accordingly, we transform the de-anonymization problem into a maximum weight matching (MWM) problem. Due to the scale of the graph, traditional algorithms for solving the MWM problem are not applicable. Instead, we propose a deep reinforcement learning-based algorithm that achieves near-optimal results. Experimental results on both real-world dataset and synthetic dataset demonstrate the effectiveness of the proposed approach.

Preserving the privacy of transactions and ensuring the regulatory compliance of transactions are two important requirements for blockchain-based financial applications. However, these two requirements are somewhat contradictory. Techniques for protecting transaction privacy, such as data encryption and zero-knowledge proof, generally make it difficult to regulate and audit the transactions. In this paper, we propose a system named zkFabLedger which enhances both the privacy and the auditability of the classic permissioned blockchain platform Hyperledger Fabric. The proposed system utilizes commitments and non-interactive zero-knowledge proofs to hide the detailed information of transactions while enabling the endorsing peer nodes to verify the regulatory compliance of transactions. Transactions are recorded on table-structured ledgers, so that the regulator can perform complex auditing of transactions. Moreover, we utilize the ring signature scheme and the secret handshake protocol to ensure the anonymity of the transaction sender while enabling the regulator to trace the sender’s identity. Simulation results demonstrate that the proposed system can balance well between privacy, regulation and efficiency.

With the help of artificial intelligence, the large amount of data generated by Internet of Things (IoT) has unleashed significant value. Federated learning is emerging as a novel paradigm which can be applied to solve the privacy issues caused by analyzing IoT data. However, traditional federated learning protocols are vulnerable to inference and poisoning attacks. Various solutions have been proposed to enhance data privacy and robustness. Nonetheless, most of these solutions are usually centralized and rely on unrealistic security assumptions. Furthermore, the recently proposed blockchain-based decentralized solutions generally incur high costs, which is unaffordable for resource-constrained IoT devices. In this article, we propose a practical secure federated learning system named PrSeFL. We utilize blockchain to decentralize the federated learning process so that the security assumptions are easier to achieve in practice. To preserve data privacy, we implement secure multiparty computation-based secure aggregation in blockchain environment. To guarantee practical robustness, we enforce norm constraints on the masked updates via zero-knowledge proof. Moreover, we propose a modified dynamic accumulator which is utilized to realize lightweight anonymous authentication of users. Simulation results show that, compared with state-of-the-art systems, PrSeFL has superior performance on authentication and model training. And the advantage of PrSeFL becomes more significant as the number of users grows.

Local differential privacy (LDP) is a promising technique to realize privacy-preserving data aggregation without a trusted aggregator. Normally, an LDP protocol requires each user to locally perturb his raw data and submit the perturbed data to the aggregator. Consequently, LDP is vulnerable to output poisoning attacks. Malicious users can skip the perturbation and submit carefully crafted data to the aggregator, altering the data aggregation results. Existing verifiable LDP protocols, which can verify the perturbation process and prevent output poisoning attacks, usually incur significant computation and communication costs, due to the use of zero-knowledge proofs. In this paper, we analyze the attacks on two classic LDP protocols for frequency estimation, namely GRR and OUE, and propose two verifiable LDP protocols. The proposed protocols are based on an interactive framework, where the user and the aggregator complete the perturbation together. By providing some additional information, which reveals nothing about the raw data but helps the verification, the user can convince the aggregator that he is incapable of launching an output poisoning attack. Simulation results demonstrate that the proposed protocols have good defensive performance and outperform existing approaches in terms of efficiency.

Nowadays the privacy issue arising in data mining applications has attracted much attention. In the context of distributed data mining, a major concern of the participant is that its privacy may be disclosed to other participants or a third party. To protect privacy, one can apply a differential privacy approach to perturb the data before sharing them with others, which generally causes a negative effect on the mining result. Thus there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a mediator builds a classifier based on the perturbed query results returned by a number of users. We propose a game theoretical approach to analyze how users choose their privacy budgets. Specifically, interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. Experimental results demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the accuracy of the classifier and the preserved privacy.

As more and more organizations deploy their sensing devices in the industrial Internet of Things network, it becomes increasingly important for the organizations to share data with others, so that the value of the data can be fully explored. However, individuals' privacy may be compromised because of data sharing. In this article, we study the differentially private data publishing problem, which aims at balancing between privacy and data utility. Specifically, two blockchain-based data publishing protocols are proposed. For histogram publishing, we propose a protocol where the Laplace noise added in the query result is verified by the blockchain. For anonymized data publishing, we propose a protocol, which can prevent the publisher and the recipient from lying about the utility of the published data. With the blockchain acting as a reliable intermediary between the publisher and the recipient, the proposed protocols can help to realize fair and auditable data sharing.

The blooming trend of smart grid deployment is engaged by the evolution of the network technology, as the connected environment offers various alternatives for electrical data collections. Having diverse data sharing/transfer means is deemed an important aspect in enabling intelligent controls/governance in smart grid. However, security and privacy concerns also are introduced while flexible communication services are provided, such as energy depletion and infrastructure mapping attacks. This paper proposes a model permissioned blockchain edge model for smart grid network (PBEM-SGN) to address the two significant issues in smart grid, privacy protections, and energy security, by means of combining blockchain and edge computing techniques. We use group signatures and covert channel authorization techniques to guarantee users' validity. An optimal security-aware strategy is constructed by smart contracts running on the blockchain. Our experiments have evaluated the effectiveness of the proposed approach.

Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.

Online social networks have now become the most popular platforms for people to share information with others. Along with this, there is a serious threat to individuals' privacy. One privacy risk comes from the sharing of co-owned data, i.e., when a user shares a data item that involves multiple users, some users' privacy may be compromised, since different users generally have different opinions on who can access the data. How to design a collaborative management mechanism to deal with such a privacy issue has recently attracted much attention. In this paper, we propose a trust-based mechanism to realize collaborative privacy management. Basically, a user decides whether or not to post a data item based on the aggregated opinion of all involved users. The trust values between users are used to weight users' opinions, and the values are updated according to users' privacy loss. Moreover, the user can make a tradeoff between data sharing and privacy preserving by tuning the parameter of the proposed mechanism. We formulate the selecting of the parameter as a multi-armed bandit problem and apply the upper confidence bound policy to solve the problem. Simulation results demonstrate that the trust-based mechanism can encourage the user to be considerate of others' privacy, and the proposed bandit approach can bring the user a high payoff.

With the development of social media technologies, sharing photos in online social networks has now become a popular way for users to maintain social connections with others. However, the rich information contained in a photo makes it easier for a malicious viewer to infer sensitive information about those who appear in the photo. How to deal with the privacy disclosure problem incurred by photo sharing has attracted much attention in recent years. When sharing a photo that involves multiple users, the publisher of the photo should take into all related users' privacy into account. In this paper, we propose a trust-based privacy preserving mechanism for sharing such coowned photos. The basic idea is to anonymize the original photo so that users who may suffer a high privacy loss from the sharing of the photo cannot be identified from the anonymized photo. The privacy loss to a user depends on how much he or she trusts the receiver of the photo. And the user's trust in the publisher is affected by privacy loss. The anonymiation result of a photo is controlled by a threshold specified by the publisher. We propose a greedy method for the publisher to tune the threshold, in the purpose of balancing between the privacy preserved by anonymization and the information shared with others. Simulation results demonstrate that the trust-based photo sharing mechanism is helpful to reduce the privacy loss, and the proposed threshold tuning method can bring a good payoff to the user.

With the growing popularity of mobile social networks, point-of-interest (POI) recommendation, which utilizes users' check-in data to suggest interesting places for users, has attracted much attention in recent years. The check-in data, containing time and location information, are closely related to the user's personal life. Due to privacy concerns, users are reluctant to share check-in data with the service provider (SP), which causes a negative effect on recommendations. It is important for the user to find a balance between privacy and recommendation quality. In this paper, we consider a POI recommendation scenario where an adversary can access the data that a user reports to the SP. The user sequentially decides whether to check in for the POI he has visited. A stochastic game model is proposed to analyze the interaction between the user and the adversary. To find a good policy for the user, two value iteration algorithms are applied. The proposed game has a large state set, which makes it difficult for policy learning. To deal with this problem, we use some tricks when implementing the minimax Q-learning algorithm, and a set of neural networks are trained to approximate the Q-functions. To evaluate the performance of the learning algorithms, we conduct a series of simulations by using real-world check-in data. Simulation results show that the proposed learning algorithms can help the user to make good decisions, in the sense that the user can get a high long-term return.

The proliferation of information technologies results in a complex network evolution of online social networks. Traditional model driven aided description cannot be appropriate for the dynamic evolution of social networks. However, in this paper, relying on the big data collected from a range of real-world online social networks, we try to explore the underlying evolution for online social networks. Firstly, we define a pair of big data driven similarity based utility models (U- models), i.e. the undirected U-model as well as the directed U-model, which can effectively reflect the statistical characteristics of online social networks. Secondly, we analyze the small-world property, scale-free property and high clustering coefficient property of our proposed U-models which consider nodes' similarity, popularity and asymmetry in a network. Finally, relying on three real-world big datasets, i.e. Sina Weibo, Tencent Weibo and Twitter, sufficient experiments show that the U-models outperform the traditional models in portraying the evolution statistical characteristic of online social networks.

The privacy issue arising in data mining applications has attracted much attention in recent years. In the context of distributed data mining, the participant can employ data perturbation techniques to protect its privacy. Data perturbation generally causes a negative effect on the mining result, which means there is a trade-off between privacy and the mining result. In this paper, we study a distributed classification scenario where a number of users provide data to a mediator to train a classifier. Interactions among users are modeled as a game in satisfaction form. And an algorithm is proposed for users to learn the satisfaction equilibrium (SE) of the game. The basis idea is that the user gradually reduces the perturbation in data until it is satisfied with the classification accuracy. Experimental results based on real data demonstrate that, when the differences among users' expectations are not significant, the proposed learning algorithm can converge to an SE, at which every user achieves a balance between the classification accuracy and the preserved privacy.

Recently, the conflict between exploiting the value of personal data and protecting individuals' privacy has attracted much attention. Personal data market provides a promising solution to this conflict, while determining the price of privacy is a tough issue. In this paper, we study the pricing problem in a setting where a data collector sequentially buys data from multiple data owners whose valuations of privacy are randomly drawn from an unknown distribution. To maximize the total payoff, the collector needs to dynamically adjust the prices offered to owners. We model the sequential decision-making problem of the collector as a multi-armed bandit problem with each arm representing a candidate price. Specifically, the privacy protection technique adopted by the collector is taken into account. Protecting privacy generally causes a negative effect on the value of data, and this effect is embodied by the time-variant distributions of the rewards associated with arms. Based on the classic upper confidence bound policy, we propose two learning policies for the bandit problem. The first policy estimates the expected reward of a price by counting how many times the price has been accepted by data owners. The second policy treats the time-variant data value as a context and uses ridge regression to estimate the rewards in different contexts. Simulation results on real-world data demonstrate that by applying the proposed policies, the collector can get a payoff which is close to that he can get by setting a fixed price, which is the best in hindsight, for all data owners.

Exploring potentially useful information from huge amount of textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristics of microblog texts, using term frequency vectors to represent microblog texts will cause “sparse data” problem. Finding proper representations of microblog texts is a challenging issue. In this paper, we apply deep networks to map the high-dimensional representations of microblog texts to low-dimensional representations. To improve the result of dimensionality reduction, we take advantage of the semantic similarity derived from two types of microblogspecific information, namely the retweet relationship and hashtags. Two types of approaches, including modifying training data and modifying the training objective of deep networks, are proposed to make use of microblog-specific information. Experiment results show that the deep models perform better than traditional dimensionality reduction methods such as latent semantic analysis and latent Dirichlet allocation topic model, and the use of microblog-specific information can help to learn better representations.

The energy consumption of a base station (BS) has attracted much attention in the study of wireless communication. Device-to-device communication, which can be utilized to offload the traffic from the BS, provides an effective way to increase network energy efficiency. How to optimally coordinate users to redistribute the traffic so as to minimize the energy consumption is an important issue. In this paper, we study two problems that are critical to this issue. First, considering that relaying data to others incurs costs to the users and different users have different costs, we propose a contract theoretical approach to design the mechanism for pricing the contributions of users. The second problem is to make a proper matching between users who demand data and users who are willing to relay data. Matching theory is exploited to deal with this problem. Specifically, we consider both interference-free and interference scenarios and develop matching algorithms, which can achieve stable matching and weak stable matching, respectively. Simulation results demonstrate the effectiveness of the proposed algorithms.

Privacy issues arising in the process of collecting, publishing and mining individuals' personal data have attracted much attention in recent years. In this paper, we consider a scenario where a data collector collects data from data providers and then publish the data to a data user. To protect data providers' privacy, the data collector performs anonymization on the data. Anonymization usually causes a decline of data utility on which the data user's profit depends, meanwhile, data providers' would provide more data if anonymity is strongly guaranteed. How to make a trade-off between privacy protection and data utility is an important question for data collector. In this paper we model the interactions among data providers/collector/user as a game, and propose a general approach to find the Nash equilibriums of the game. To elaborate the analysis, we also present a specific game formulation which takes k-anonymity as the anonymization method. Simulation results show that the game theoretical analysis can help the data collector to deal with the privacy-utility trade-off.