As a promising computing paradigm, edge-cloud computing network integrates the ubiquitous computing resources on the cloud and edge servers to provide high-quality services, but is susceptible to complicated distributed denial-of-service (DDoS) attacks. Although some works have studied edge DDoS mitigation, the cooperative defense among cloud and edge servers considering the DDoS attacker’s strategic attack strategy is yet to be explored. In this paper, we model the interactions between the DDoS attacker and defenders as a two-stage dynamic game and propose a distributed cooperative defense scheme. In Stage I, the DDoS attacker strategically launches different amounts of malicious traffic to different edge servers to maximize the total filtering cost. In Stage II, edge servers under attack (i.e., defenders) filter their traffic on the cloud and other edge servers cooperatively to minimize the total filtering cost. The defenders’ problem in Stage II is NP-hard and we solve the problem by modeling defenders’ behaviors as a selfish filtering game. We prove that the selfish filtering game admits a unique Nash equilibrium (NE) with guaranteed social efficiency, and design both a centralized algorithm and a distributed algorithm to calculate the NE. For the attacker’s problem in Stage I, we first analyze a special case where each edge server has the same amount of normal traffic, and design a low-complexity algorithm to calculate the optimal attack strategy. We then analyze the general case where edge servers have different amounts of normal traffic, for which we derive the approximate optimal solution. Simulation results show that the DDoS attacker tends to launch attacks to all edge servers to reduce their cooperative defense capability, and our proposed distributed cooperative defense mechanism can effectively reduce the total filtering cost compared with existing benchmark defense mechanisms.

A user with limited computation resources can address his delay-sensitive and computation-intensive tasks through task offloading to nearby edge servers, by purchasing both network and computation resources from profitseeking providers. We identify a substitutability property of computation and network resources for realizing the delay requirement. That is, to reduce task delay, the user can purchase more network resources to reduce transmission delay or more computation resources to reduce computation delay. This property significantly affects the user's purchase behavior and leads to strategic interactions between the computation service provider (CSP) and the network service provider (NSP), which have not been systematically studied yet. To this end, we formulate a two-stage Stackelberg game. In Stage I, one CSP and one NSP set their prices. In Stage II, each user decides offloading ratio and the amount of resources to purchase. By deriving the closed-form solutions in Stage II, we analytically conclude that the substitutability affects the user's decision through the network price to computation price ratio. We then incorporate the solution in Stage II into Stage I and analyze the service providers' pricing under two market structures. In the cooperative setting, where two service providers are integrated and jointly maximize their total profit, they would flexibly adjust the price ratio based on computation and network costs. In the competitive setting, where they are separate firms and aim to maximize their own profit, we formulate a pricing game and characterize a counter-intuitive equilibrium: the service providers would set high prices instead of low prices. Experimental results show that users benefit from service providers' competitive interactions.

Federated learning (FL) has been extensively applied in industrial cyber-physical systems (ICPSs) to develop powerful models for complex industrial tasks (e.g., fault diagnosis), while safeguarding industrial data confidentiality. Asynchronous federated learning (AFL) effectively mitigates the straggler issue in the synchronous paradigm by aggregating client models in a first-come-first-served manner. Proper client selection is crucial for achieving efficient model training in AFL. A widely adopted model for implementing client selection in AFL is multi-armed bandit (MAB), which models client selection as arm pulling. Existing MAB-based client selection schemes overlook practical scenarios where direct client-server communications are unfavorable or unavailable (for example, in ICPSs such as mines, where communication infrastructure is underdeveloped, direct client-server communication is often unreliable or even unfeasible). In such cases, the server needs to incentivize self-interested relays to perform arm-pulling actions, including selecting the right client and relaying the communication from the selected client to the server. This paper proposes the first framework of incentivized online client selection for AFL. The design and optimization of such a framework involve significant challenges due to the tight coupling between unknown client behavior and private relay cost. To circumvent this challenge, we adopt the dual-based method and construct a special Lagrangian function that incorporates client behavior learning and relay cost revelation, and utilize it to design a socially-optimal mechanism for the framework. Our mechanism satisfies several desirable properties, including voluntary participation, incentive compatibility, relay utilization fairness, and client participation fairness. The proposed mechanism achieves the same asymptotic performance as the state-of-the-art benchmark that requires additional information. Furthermore, our analysis reveals that more available relays bring our mechanism closer to the theoretical upper bound of social performance. Numerical results demonstrate that our proposed mechanism achieves up to 85% and 99% of the social welfare obtained by the benchmarks.

In cross-silo federated learning (FL), clients of common interest cooperatively train a global model without sharing local sensitive data, but they still face potential privacy leakage due to privacy threats from malicious attackers. Although some articles have proposed effective privacy-preserving mechanisms for FL (such as differential privacy (DP)), clients in cross-silo FL are usually different companies or organizations who may behave selfishly to optimize their own benefits. In this article, we study DP-based cross-silo FL where clients selfishly decide their participation levels (i.e., data sizes for model trainings) and privacy leakage tolerance levels to trade off between model accuracy loss and privacy loss, and we model clients’ interactions as a participation-dependent privacy preservation game. It is challenging to analyze the game since the comprehensive impact of participation levels and privacy leakage tolerance levels on model accuracy is unclear and the behaviors of heterogeneous clients are coupled in a highly complex manner. To capture the impact of participation and privacy preservation behaviors, we first characterize the optimality gap of DP-based cross-silo FL for both convex and non-convex models, where the privacy leakage tolerance levels and the participation levels are coupled nonlinearly. We model clients’ costs based on the optimality gap, and prove that clients’ selfish participation-dependent privacy preservation game is a potential game. To analyze the optimal strategies of heterogeneous clients in a stable state, we derive the closed-form expression for the unique Nash equilibrium (NE), where clients may choose full participation or partial participation, and the equilibrium privacy preservation strategy depends on clients’ accuracy-privacy preference ratios. We analyze the social efficiency of the NE by calculating the price of anarchy (PoA) and show that the PoA increases with the number of clients and the heterogeneity of clients’ model accuracy preferences. To improve the social efficiency achieved at equilibrium, we design a socially efficient incentive mechanism that allows clients with large model accuracy preferences to compensate clients with small model accuracy preferences. Extensive experiments verify our theoretical results for both the convex and non-convex models as well as both the i.i.d. data distribution case and the non-i.i.d. data distribution case.

Federated learning is a popular distributed machine learning paradigm that keeps data locally at clients. To further enhance privacy protection, differential privacy techniques are incorporated in the federated learning framework. We can quantify the privacy budget (or privacy protection level) through differential privacy and allocate the budget to different communication rounds according to the composition property of differential privacy. Recent works have shown that suitably allocating budgets to different iterations can improve model performance. How to allocate privacy budgets in different communication rounds for different clients in the federated learning framework is a significant problem to study. The problem is challenging to solve due to the unknown relationship between noise levels and the model accuracy and the coupling property of the clients' decisions. In this paper, we propose a method based on multi-agent reinforcement learning to solve the privacy budget allocation problem, which maximizes the accuracy of the federated learning model given limited privacy budgets for the clients. The experiments show that our proposed method is better than the uniform allocation, arithmetic sequence allocation, and exponential allocation methods.

Deep learning classifiers are crucial in the age of artificial intelligence. The device-edge-based collaborative inference has been widely adopted as an efficient framework for promoting its applications in IoT and 5G/6G networks. However, it suffers from accuracy degradation under non-i.i.d. data distribution and privacy disclosure. For accuracy degradation, direct use of transfer learning and split learning is high cost and privacy issues remain. For privacy disclosure, cryptography-based approaches lead to a huge overhead. Other lightweight methods assume that the ground truth is non-sensitive and can be exposed. But for many applications, the ground truth is the user's crucial privacy-sensitive information. In this paper, we propose a framework of Roulette, which is a task-oriented semantic privacy-preserving collaborative inference framework for deep learning classifiers. More than input data, we treat the ground truth of the data as private information. We develop a novel paradigm of split learning where the back-end DNN is frozen and the front-end DNN is retrained to be both a feature extractor and an encryptor. Moreover, we provide a differential privacy guarantee and analyze the hardness of ground truth inference attacks. To validate the proposed Roulette, we conduct extensive performance evaluations using realistic datasets, which demonstrate that Roulette can effectively defend against various attacks and meanwhile achieve good model accuracy. In a situation where the non-i.i.d. is very severe, Roulette improves the inference accuracy by 21% averaged over benchmarks, while making the accuracy of discrimination attacks almost equivalent to random guessing.

Federated learning (FL) provides a collaborative paradigm for distributedly training a global model while protecting clients' privacy. In addition to communication bottlenecks and non-i.i.d. data distributions, the FL framework introduces two fundamental economic challenges: first, clients are self-interested and strategic in practice, requiring specific incentives to participate in FL; second, each client can misreport its private information to its advantage. Although existing studies have proposed economic mechanisms, they are often restricted to a “binary” participation scenario, leading to communication overheads or biased models due to client heterogeneity. In this paper, we first analyze the convergence bound under arbitrary client sampling probability with a varying number of clients. Then, we consider an optimal mechanism design problem: the FL convergence bound minimization subject to budget constraint, incentive compatibility, and individual rationality. We derive the optimal sampling probability function in a close form. To overcome the unknown prior distribution challenge, we introduce a prior-independent mechanism design, and show how it gradually learns cost distributions by exploiting the incentive compatibility property. We perform extensive experiments and show that, while outperforming the uniform sampling scheme, two proposed schemes (prior-based and prior-independent ones) perform closely to the ideal complete information upper bound.

Cross-silo federated learning (FL) is a distributed learning method where clients collaboratively train a global model without exchanging local data. However, recent works reveal that potential privacy leakage occurs when clients upload their local updates. Although some works have studied privacy-preserving mechanisms in FL, the selfish privacy-preserving behaviors of clients (who are usually cost-sensitive companies or organizations) are yet to be explored. In this paper, we formulate clients' privacy-preserving behaviors in cross-silo FL as a multi-stage privacy preservation game, where each stage game corresponds to one training iteration. Specifically, clients selfishly perturb their local updates in each training iteration to trade off between convergence performance and privacy loss. To analyze the game, we first derive a novel theoretical bound to characterize the impact of clients' local perturbations on the convergence of FL through analyzing the corrective effect of gradient descent in model training. With the novel convergence bound, we prove that each stage game is a potential game with a unique Nash equilibrium (NE) and the multi-stage privacy preservation game admits a unique subgame perfect Nash equilibrium (SPNE). We show that at the SPNE, the magnitude of each client's local perturbation decreases geometrically with training iterations. We then characterize the efficiency of the SPNE in terms of social cost by the price of anarchy (PoA), and show that the efficiency decreases with the number of clients in some cases. To tackle this problem, we propose a socially efficient incentive mechanism that allows monetary transfer among clients and guarantees individual rationality, budget balance, and social efficiency. To further elicit the private information from the selfish clients, we propose a truthful mechanism that achieves approximate social efficiency. Simulation results show that our proposed mechanisms are effective even when clients are highly heterogeneous, and can decrease clients' total cost by up to 58.08% compared with that at the SPNE.

Collaborative Edge Computing (CEC) is an emerging paradigm that collaborates heterogeneous edge devices as a resource pool to compute DNN inference tasks in proximity such as edge video analytics. Nevertheless, as the key knob to improve network utility in CEC, existing works mainly focus on the workload routing strategies among edge devices with the aim of minimizing the routing cost, remaining an open question for joint workload allocation and routing optimization problem from a system perspective. To this end, this paper presents a holistic, learned optimization for CEC towards maximizing the total network utility in an online manner, even though the utility functions of task input rates are unknown a priori. In particular, we characterize the CEC system in a flow model and formulate an online learning problem in a form of cross-layer optimization. We propose a nested-loop algorithm to solve workload allocation and distributed routing iteratively, using the tools of gradient sampling and online mirror descent. To improve the convergence rate over the nested-loop version, we further devise a single-loop algorithm. Rigorous analysis is provided to show its inherent convexity, efficient convergence, as well as algorithmic optimality. Finally, extensive numerical simulations demonstrate the superior performance of our solutions.

Federated learning (FL) enables multiple data owners to collaboratively train machine learning (ML) models for different model requesters while keeping data localized. Thus, FL can mitigate privacy leakage in conventional data marketplaces for ML applications requiring raw data trading for centralized model training. Nevertheless, data owners involved in FL may still suffer potential privacy leakage from gradient exposure to the model requesters. In this work, we advocate a novel data marketplace with differentially private federated learning (DPFL) to reduce such threats and maximize the social welfare. Designing such a marketplace involves several challenges. First, it is difficult to determine the privacy budget that a data owner should choose for a model requester, since they have conflicting objectives and private utility/cost information. Second, each data owner sustains privacy costs from his friends’ participation in DPFL due to data correlations, which introduces a negative externality to the market. We design a social-aware iterative double auction (SARDA) mechanism to resolve these challenges and achieve socially optimal market operation. SARDA employs a broker to coordinate the interactions between data owners and model requesters and induces them to truthfully report by iteratively updating the allocation and pricing rules. Moreover, SARDA accounts for the negative externality by incorporating others’ bids to reimburse each data owner. We show that SARDA achieves the optimal social performance and creates up to$60\%$higher social welfare than the social-agnostic benchmark.

Federated learning (FL) is a distributed machine learning scheme in which clients jointly train a model without exposing their private data to a central server. However, two challenges exist: one technical challenge of the non-IID issue and one economic challenge of the incentive issue. Many existing works presented incentive mechanisms to select clients with high-quality data to tackle the non-IID issue. However, the existing works assumed the server's availability of clients' true data quality information. We notice that this assumption is hard to satisfy due to the private nature of the information. In this paper, we try to eliminate this assumption and adopt a local differentially private mechanism in the incentive mechanism. In this regard, we propose a Bayesian-based method for the server to estimate the clients' qualities and an efficient algorithm that incentivizes clients with approximately high-quality data. We prove that our solution has an approximation guarantee and is incentive-compatible, individually rational, and computationally efficient. We also analyze the quality loss due to the integration of the privacy-preserving mechanism. We conduct extensive experiments and show that our proposed solution outperforms the mechanism without considering the non-IID issue and is comparable to the mechanism without privacy protection.

Data reporters have privacy concerns when they are requested to contribute personal data to a data collector. Such privacy concerns are strengthened by data correlation and social relationship, as the data correlation could inevitably cause privacy issues to their socially-connected individuals who even do not report the data. However, both factors are hard to quantify precisely in practice due to their private nature. Such an incomplete information situation poses great challenges for the data reporters to determine their coupled privacy-preserving strategies and for the data collector to choose a proper privacy-preserving mechanism. This motivates us to propose a novel Bayesian game-theoretic framework to analyze the data reporters’ behaviors. We show that the game has a symmetric Bayesian Nash Equilibrium (BNE) with a threshold structure, which builds a connection between the data reporter’s action and privacy concern under incomplete information. The complicated relationship between the BNE and the data collector’s strategy makes it difficult to solve the data collector’s optimization problem. However, by exploiting the unimodal feature of the problem, we present a low-complexity algorithm to compute the optimal privacy-preserving mechanism. Through analytical and numerical studies, we find that the lack of complete information could cause the data reporters to adopt more conservative strategies but make the data collector adopt a less conservative mechanism, resulting in an overall privacy protection degradation. The simulations further demonstrate that the degradation could be alleviated by stronger data correlation and social relationship, and a higher probability of serious privacy concerns.

Existing machine learning (ML) model marketplaces generally require data owners to share their raw data, leading to serious privacy concerns. Federated learning (FL) can partially alleviate this issue by enabling model training without raw data exchange. However, data owners are still susceptible to privacy leakage from gradient exposure in FL, which discourages their participation. In this work, we advocate a novel differentially private FL (DPFL)-based ML model marketplace. We focus on the broker-centric design. Specifically, the broker first incentivizes data owners to participate in model training via DPFL by offering privacy protection as per their privacy budgets and explicitly accounting for their privacy costs. Then, it conducts optimal model versioning and pricing to sell the obtained model versions to model buyers. In particular, we focus on the broker’s profit maximization, which is challenging due to the significant difficulties in the revenue characterization of model trading and the cost estimation of DPFL model training. We propose a two-layer optimization framework to address it, i.e., revenue maximization and cost minimization under model quality constraints. The latter is still challenging due to its non-convexity and integer constraints. We hence propose efficient algorithms, and their performances are both theoretically guaranteed and empirically validated.

In an online social network, users exhibit personal information to enjoy social interaction. The social network provider (SNP) exploits users’ information for revenue generation through targeted advertisement, in which the SNP presents advertisements to proper users effectively. Therefore, an advertiser is more willing to pay for targeted advertisement to promote his product. However, the over-exploitation of users’ information would invade users’ privacy, which would negatively impact users’ social activeness. Motivated by this, we study the privacy policy (policies) of the SNP(s) with targeted advertisement, in both monopoly and duopoly markets. We characterize the privacy policy in terms of the fraction of users’ information that the provider should exploit, and formulate the interactions among users, advertiser, and SNP(s) as a three-stage Stackelberg game. By leveraging the model’s supermodularity property, we prove the threshold structure of users’ equilibrium information levels. We discover the overall information that can be exploited by an SNP is non-monotonic in the exploitation fraction. Monopoly (one SNP) study shows our proposed optimal privacy policy helps the SNP earn even more advertisement revenue than full exploitation policy does. The situation of the duopoly market is much more complicated. In that case, if the service quality gap between the two SNPs is large, the stronger SNP will choose a conservative privacy protection policy that drives the other SNP out of the market. However, if the service quality gap is small and the advertisement revenue is promising, the stronger SNP would choose an aggressive policy to exploit the advertisement revenue and both SNPs will have positive market shares.

While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox'' is that when an individual shares her data, it is not just her privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this paper, we study the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We design an incentive compatible mechanism that optimizes the worst-case trade-off between bias and variance of the estimation subject to a budget constraint, where the worst-case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and non-monotonicity properties of the marketplace.

In an online social network, users exhibit personal information to enjoy social interaction. The social network provider (SNP) exploits users' information for revenue generation through targeted advertising. The SNP can present ads to proper users efficiently. Therefore, an advertiser is more willing to pay for targeted advertising. However, the over-exploitation of users' information would invade users' privacy, which would negatively impact users' social activeness. Motivated by this, we study the optimal privacy policy of the SNP with targeted advertising business. We characterize the privacy policy in terms of the fraction of users' information that the provider should exploit, and formulate the interactions among users, advertiser, and SNP as a three-stage Stackelberg game. By carefully leveraging supermodularity property, we reveal from the equilibrium analysis that higher information exploitation will discourage users from exhibiting information, lowering the overall amount of exploited information and harming advertising revenue. We further characterize the optimal privacy policy based on the connection between users' information levels and privacy policy. Numerical results reveal some useful insights that the optimal policy can well balance the users' trade-off between social benefit and privacy loss.

We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data from individuals to perform some computations. To relieve the privacy threat to the contributors, the data collector adopts a privacy-preserving mechanism by adding random noise to the computation result, at the cost of reduced accuracy. Individuals decide whether to contribute data when faced with the privacy issue. Due to the intrinsic uncertainty in privacy protection, we model individuals' privacy-related decision using Prospect Theory. Such a theory more accurately models individuals' behavior under uncertainty than the traditional expected utility theory, whose prediction always deviates from practical human behavior. We show that the data collector's utility maximization problem involves a polynomial of high and fractional order, the root of which is difficult to compute analytically. We get around this issue by considering a large population approximation, and obtain a closed-form solution that well approximates the precise solution. We discover that the data collector who considers the more realistic Prospect Theory based individual decision modeling would adopt a more conservative privacy-preserving mechanism, compared with the case based on the expected utility theory modeling. We also study the impact of Prospect Theory parameters, and concludes that more loss-averse or risk-seeking individuals will trigger a more conservative mechanism. When individuals have different Prospect Theory parameters, simulations demonstrate that the privacy protection first becomes stronger and then becomes weaker as the heterogeneity increases from a low value to a high one.

We study a privacy-preserving data collection problem by considering individuals' data correlation and social relationship. A data collector gathers data from some data reporters to perform certain analysis with a privacy-preserving mechanism. Due to the data correlation, the analysis will cause privacy leakage not only to the data reporters but also to those individuals who do not report data. Owing to the social relationship among them, the data reporters would consider the possibility of adding some random noise to the reported data to reduce the privacy leakage. The privacy loss of the individuals (both data reporters and non-reporters) depend on all the data reporters' strategies, which naturally leads to a game theoretical analysis. A key result shows that the data reporters can be ordered based on their levels of joint considerations of social relationship and data correlation, and at the Nash Equilibrium of the game at most one data reporter with the most significant consideration may add noise to the reported data. We design an efficient algorithm for the data collector to construct the data reporter set, and derive the optimal privacy-preserving mechanism to ensure all the data reporters' truthful reporting. We conduct extensive simulations with the Facebook social data to demonstrate some insights: It is optimal for the data collector to adopt a more conservative mechanism when the data correlation or the social relationship is stronger. Compared with the data correlation information, the social network information plays a more critical role in the data collector's utility maximization problem.

We study a privacy-preserving data collection problem, by jointly considering data reporters' data correlation and social relationship. A data collector gathers data from individuals to perform a certain analysis with a privacy-preserving mechanism. Due to data correlation, the data analysis based on the reported data can cause privacy leakage to other individuals (even if they do not report data). The data reporters will take such a privacy threat into account, owing to the social relationship among individuals. This motivates us to formulate a two-stage Stackelberg game: In Stage I, the data collector selects some individuals as data reporters and designs a privacy-preserving mechanism for a sum query analysis. In Stage II, the selected data reporters contribute their data with possible perturbations (through adding noise). By analyzing the data reporters' equilibrium decisions in Stage II, we show that given any fixed reporter set, only one data reporter with the most significant joint consideration of the social relationship and data correlation may add noise to his reported data. The rest of the data reporters will truthfully report their data. In Stage I, we derive the data collector's optimal privacy-preserving mechanism and propose an efficient algorithm to select the data reporters. We conclude that the data collector should jointly capture the impact of data correlation and social relation to ensure all data reporters truthfully reporting their data. We conduct extensive simulations based on random network and real-world social data to investigate the impact of data correlation and social network on the system. We find that the availability of social network information is more critical to the data collector compared with data correlation information.

We study a mechanism design problem of privacy- preserving data collection with privacy protection uncertainty. A data collector wants to collect enough data to perform a certain computation that benefits the individuals who contribute the data, with the possibility of individual privacy leakage. The data collector adopts a privacy-preserving mechanism by adding some random noise to the computation result, which reduces the accuracy of the computation. Individuals decide whether to contribute data based on the potential benefit and the possible privacy cost induced by the mechanism. Due to the intrinsic uncertainty involved in privacy protection, we model individuals' privacy-aware participation using the prospect theory, which more accurately models individuals' behavior under uncertainty than the traditional expected utility theory. We show that the data collector's utility maximization problem involves a polynomial of high and fractional order, which is difficult to solve analytically. We get around this issue by proposing an approximation method, which allows us to obtain a closed form unique solution of the data collector's decision problem. We numerically show that the approximation error is small when the number of individuals is large. By comparing with the results under the expected utility theory, we conclude that a data collector who considers the more realistic prospect theory modeling should adopt a stricter privacy-preserving mechanism to boost her utility.