Game theory establishes a fundamental framework for analyzing strategic interactions among rational decision-makers. The rapid advancement of large language models (LLMs) has sparked extensive research exploring the intersection of these two fields. Specifically, game-theoretic methods are being applied to evaluate and enhance LLM capabilities, while LLMs themselves are reshaping classic game models. This paper presents a comprehensive survey of the intersection of these fields, exploring a bidirectional relationship from three perspectives: (1) Establishing standardized game-based benchmarks for evaluating LLM behavior; (2) Leveraging game-theoretic methods to improve LLM performance through algorithmic innovations; (3) Characterizing the societal impacts of LLMs through game modeling. Among these three aspects, we also highlight how the equilibrium analysis for traditional game models is impacted by LLMs' advanced language understanding, which in turn extends the study of game theory. Finally, we identify key challenges and future research directions, assessing their feasibility based on the current state of the field. By bridging theoretical rigor with emerging AI capabilities, this survey aims to foster interdisciplinary collaboration and drive progress in this evolving research area.

A parallel acceleration strategy and hardware implementation scheme for SAR imaging is proposed to accelerate imaging in high bandwidth, large aperture, and high-density scenarios. Based on traditional SAR imaging, this strategy divides the target image into multiple local images, selecting appropriate radar data within a suitable aperture range based on the locations of each local component. Multiple SAR imaging units operate in parallel, and the local images are stitched together in their original locations to achieve accelerated imaging after cropping. This paper designs the hardware for the SAR imaging units, with its core FFT operations designed as a low-cost reusable structure. By reusing and expanding this hardware unit, the acceleration system can be built with relatively low hardware resources and minimal loss in imaging quality, demonstrating significant application potential for THz radar imaging and high-resolution large-aperture security inspections.

In the digital age, resources such as open-source software and publicly accessible databases form a crucial category of digital public goods, providing extensive benefits for Internet. However, these public goods' inherent non-exclusivity and non-competitiveness frequently result in under-provision, a dilemma exacerbated by individuals' tendency to free-ride. This scenario fosters both cooperation and competition among users, leading to the public goods games. This paper investigates networked public goods games involving heterogeneous players and convex costs, focusing on the characterization of Nash Equilibrium (NE). In these games, each player can choose her effort level, representing her contributions to public goods. Network structures are employed to model the interactions among participants. Each player's utility consists of a concave value component, influenced by the collective efforts of all players, and a convex cost component, determined solely by the individual's own effort. To the best of our knowledge, this study is the first to explore the networked public goods game with convex costs. Our research begins by examining welfare solutions aimed at maximizing social welfare and ensuring the convergence of pseudo-gradient ascent dynamics. We establish the presence of NE in this model and provide an in-depth analysis of the conditions under which NE is unique. We also delve into comparative statics, an essential tool in economics, to evaluate how slight modifications in the model--interpreted as monetary redistribution--affect player utilities. In addition, we analyze a particular scenario with a predefined game structure, illustrating the practical relevance of our theoretical insights. Overall, our research enhances the broader understanding of strategic interactions and structural dynamics in networked public goods games, with significant implications for policy design in internet economic and social networks.

Evaluating cryptographic implementations with respect to side-channel analysis (SCA) has been mandated at high security levels. Typically, the evaluation involves four stages: detection, modeling, certification and recovery. In pursuit of a specific goal at each stage, inherently different techniques were previously considered necessary. However, since the recent Eurocrypt 2022 and Eurocrypt 2024, linear regression analysis (LRA) has become the unique technique well-applied throughout all the stages. In this paper, we concentrate on this “silver bullet” technique within the field of SCA. In the first part of this paper, we answer three fundamental questions organized progressively. The first one relates to “why use LRA?”. Our discussion of the nominal and binary nature elucidates its critical role in underpinning the state-of-the-art techniques. Having understood the merits, a natural follow-up is “how to use it (correctly and effectively)?”. A theoretical analysis of the design matrix is provided, regarding the sample distribution of plaintext and the chosen degree of polynomial. We summarize the conditions for eliminating multicollinearity, a problem that can be harmful to all LRA-based techniques. The last question “who should use LRA?” reveals an intriguing evaluator-advantageous property: LRA can only unleash its full potential when the key is known. In the second part of this paper, we clarify the connections between LRA and traditional SCA techniques. Our proofs provide new insights into the prior investigation of SCA reduction, fostering a comprehensive understanding of this linear family. The conclusions suggest that the core working mechanisms of the state-of-the-art techniques can be traced back to those of earlier differential side-channel analyses. Experimental results are in line with the theory, confirming its correctness in practice.

Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight.

We consider a hierarchical structure of a permissioned blockchain with three types of participant: providers, collectors, and governors. Providers forward transactions to collectors; collectors upload received transactions to governors after verifying and labeling them; and governors validate a portion of the labeled transactions they receive, pack valid transactions into a block, and append the block to the ledger. This model has various fields of application including data collection from the Internet-of-Things and second-hand markets. Our main contribution is to propose a reputation-based protocol to help governors evaluate the reliability of collectors. Specifically, given a transaction, each governor runs a softmax-based function to calculate a probability for each collector that sent and labeled this transaction. The probabilities, calculated using collectors’ reputations as inputs, represent the likelihood of the lead governor selecting the labeled transaction from collectors to consider for further validation. After the lead governor verifies a transaction, all collectors’ reputations are updated in line with the agreement of their labeling and the validity of the transaction as found by the lead governor. We show, both theoretically and empirically, that our protocol can significantly reduce governors’ verification workloads while maintaining firm liveness and high incentives.

With the development of blockchain applications, the requirements for file storage in blockchain are increasing rapidly. Many protocols, including Filecoin, Arweave, and Sia, have been proposed to provide scalable decentralized file storage for blockchain applications. However, the reliability is not well promised by existing protocols. Inspired by the idea of insurance, we innovatively propose a decentralized file storage protocol in blockchain, named as FileInsurer, to achieve both scalability and reliability. While ensuring scalability by distributed storage, FileInsurer guarantees reliability by enhancing robustness and fully compensating for the file loss. Specifically, under mild conditions, we prove that no more than 0.1% value of all files should be compensated even if half of the storage collapses. Therefore, only a relatively small deposit needs to be pledged by storage providers to cover the potential file loss. Because of lower burdens of deposit, storage providers have more incentives to participate in the storage network. FileInsurer can run in the top layer of the InterPlanetary File System (IPFS), and thus it can be directly applied in Web 3.0, Non-Fungible Tokens, and Metaverse.

Public goods projects, including open source technology, client development, and blockchain knowledge education, play an important role in the flourishing blockchain ecosystem. Accordingly, decision making for public goods funding is a key issue in the studies of the blockchain ecosystem. This work develops a human oracle protocol approach, involved with public goods projects, experts, and funders, as a solution to the public goods investment problem on blockchain. In our human oracle, funders contribute their investments, which are stored in a funding pool. Experts provide investment advice on public goods projects based on their experience. Decisions made by the human oracle on the amount of support from the funding pool are based on experts’ reputation. The reputation of each expert is updated by the performance of the project’s implementation in comparison to her advice. That is, better investment performance brings a higher reputation. Besides being applied to static model, our human oracle can also be extended to accommodate dynamic settings, in which the experts might leave or join the decision-making process. We introduce a regret bound to measure the effectiveness of our human oracle. Theoretically, we prove an upper regret bound for both static and dynamic models, and prove its tightness with an asymptotically equal lower bound. Empirically, we show that our oracle’s investment decision is close to the optimal investment in hindsight.

The Internet-scale peer-to-peer (P2P) systems usually build their success on distributed protocols. For example, the well-known BitTorrent network for resource exchange is based on the proportional response protocol, where each participant exchanges its resources with its neighbors in proportion to what it has received in the previous round. The dynamics of such a protocol has been proved to converge to a market equilibrium. On the other hand, it requires thorough incentive analysis to show the robustness of such protocol, as the distributed agents may strategically manipulate the system once they are able to benefit. Recent studies have developed strategyproofness results of the proportional response protocol against agent deviations in the forms of weight cheating and edge deleting. However, the protocol is not truthful against Sybil attacks, under which an agent may create several fictitious identities and control these fictitious identities to exchange resources with others. In this paper, we apply the concept of incentive ratio to measure how much the utility of a strategic agent in a market equilibrium can be improved by playing Sybil attacks. We prove a tight incentive ratio of two for any agent launching Sybil attacks over general networks. The tight incentive ratio of two closes an open problem modeling the successful tit-for-tat protocol for Internet resource exchanging and also presents a complete picture in this line of theoretical studies with real applications.

Blockchain protocols require nodes to verify all received transactions before forwarding them. However, massive spam transactions cause the participants in blockchain systems to consume many resources in verifying and propagating transactions. This paper proposes a reputation-based mechanism to increase the efficiency of processing transactions by considering the reputations of the sending nodes. Reputations are in turn adjusted based on the quality of transaction processing. Our proposed reputation-based mechanism offers three main contributions. First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified. Second, we optimize the transaction forwarding protocol to reduce propagation delay by prioritizing forwarding transactions to reputable receivers. Third, we design a data request protocol that provides alternative data exchange methods for nodes with different reputations. A series of simulations demonstrate the performance of our reputation-based mechanism.

In this article, we discuss the Sybil attack on a sharing-based economic system where each participant contributes its own resource for all to share. Such an attack is possible especially in the cloud computing model where agents can exchange information with the cloud and obtain aggregated information from it. We are interested in the robustness of the market equilibrium mechanism against such an attack. We adopt the incentive ratio to measure the gain that a participant can make by splitting its identity and reconstructing communication connections with others. On one hand, we show that no player can increase more than$\sqrt{2}$times its original share in the market equilibrium solution by characterizing the worst case, in which a strategic agent can obtain the maximal gain in utility by playing the Sybil attack. On the other hand, the bound of$\sqrt{2}$is proved to be tight by constructing a proper instance. We also simulate on a series of random graphs and observe that the incentive ratio was no more than two in the general setting.

Transportation sharing in goods (bike sharing), distinguished from service sharing (ride), has been one of the most active sectors of the sharing economy recently. Such a business model, facilitated by the mobile Internet and cloud computing platforms, seeks supplies in vehicles on demands at matched times and rental locations. The success has attracted more competitors into it, counter-effectively resulted in redundancies in total supplies, reducing social efficiency. In this work, we take electric car sharing as an example to propose a business solution to deal with such a dilemma. Our main idea is to set up a joint venture to provide shared electric cars for different competitors to operate on. These competitors provide their differentiated service for their customers through their own electric mobile Apps, while reducing the infrastructure cost through the joint venture. We study this business model as a two-stage Stackelberg game to analyze the optimal pricing and the sharing scheme of the leader (joint venture) and its followers (car sharing operators). Our model places the sharing of the electric vehicles in two tiers: One among the customers (to reduce the cost of time sharing) and the other among the operators (to reduce the cost of space sharing).

Permissioned blockchains take more reliability on participants than permissionless ones. In this poster, we focus on a hierarchical scenario of permissioned blockchains, which includes three types of participants: providers, collectors, and governors. Such a scenario has many applications in the field of IoT data collection, horizontal strategic alliances, etc. Our object is to reduce the cost of the governor's transaction verification. For this purpose, we propose a reputation protocol to help the governor measure the reliability of collectors. Based on the measurement of collectors' reputations, governors can pack high-quality transactions from reliable collectors into blocks, and thus the cost of verifying transactions can be decreased effectively. Through theoretical analysis, our protocol dramatically reduces the verification loss of governors.

For a blockchain system, the network layer is of great importance for scalability and security. The critical task of blockchain networks is to provide a fast delivery of data. A rapid spread accelerates the transactions to be included into blocks and then confirmed. Existing blockchain systems, especially the cryptocurrencies like Bitcoin, take a simple strategy that requires relay nodes to verify all received transactions and then forward valid ones to all outbound neighbors. Unfortunately, this design is inefficient and slows down the transmission of transactions. In this paper, we introduce the concept of reputation and propose a novel relay protocol, RepuLay, to accelerate the transmission of transactions across the network. First of all, we design a reputation mechanism to help each node identify the unreliable and inactive neighbors. In this mechanism, two values are used to define one’s reputation. Each node keeps a local list of reputations of all its neighbors. Based on the reputation mechanism, RepuLay adopts probabilistic strategies to process transactions. More specifically, after receiving a transaction, the relay node verifies it with a certain probability, which is deduced from the first value of sender’s reputation. Next, the valid and unverified transactions are forwarded to some neighbors. Each neighbor has some probability to be chosen as a receiver and the probability is determined by its second value of reputation. Theoretically, we prove that our design can guarantee the quality of relayed transactions. Further simulation results confirm that RepuLay effectively accelerates the spread of transactions and optimize the usage of nodes’ bandwidths.

Persistent Fault Attack (PFA) is a recently proposed Fault Attack (FA) method in CHES 2018. It is able to recover full AES secret key in the Single-Byte-Fault scenario. It is demonstrated that classical FA countermeasures, such as Dual Modular Redundancy (DMR) and mask protection, are unable to thwart PFA. In this paper, we propose a fast-detection and fault-correction algorithm to prevent PFA. We construct a fixed input and output pair to detect faults rapidly. Then we build two extra redundant tables to store the relationship between the adjacent elements in the S-box, by which the algorithm can correct the faulty elements in the S-box. Our experimental results show that our algorithm can effectively prevent PFA in both Single-Byte-Fault and Multiple-Bytes-Faults scenarios. Compared with the classical FA countermeasures, our algorithm has a much better effect against PFA. Further, the time cost of our algorithm is 40% lower than the classical FA countermeasures.

Fundamental issues in resource sharing over large scale networks have gained much attention from the research community, in response to the growth of sharing economy over the Internet and mobile networks. We are particularly interested in the fundamental file sharing and subsequently P2P network bandwidth sharing developed by BitTorrent and later formalized by Wu and Zhang [15] as the proportional response protocol. It is of practical importance in the design to provide agent incentives to follow the distributed protocol out of their own rationality. We study the robustness of the distributed protocol in this incentive issue against a Sybil attack, a common type of grave threat in P2P network. For the resource sharing on rings, and we characterize the utility gain from a Sybil attack in the concept of incentive ratio. Previous works proved the incentive ratio is lower bounded by two and upper bounded by four, and later the upper bound is improved to three. It has been listed in [5] and [9] as an open problem to tighten them. In this paper, we completely resolve this open problem with a better understanding on the influence from different class agents to the resource allocation under the distributed protocol.

As one of the fastest-growing applications in the Peer-to-Peer (P2P) network, the development of blockchain technology is accompanied by different attacks. Those include whitewashing, free-riding, and distributed denial of service (DDoS) attacks, particularly because of features such as anonymity, distributed, permissionless in the blockchain network. One popular of them is spam transactions. Although the blockchain protocol requires each node to verify all received transactions, many nodes choose to forward transactions without verification to conserve their computational power, as there is no punishment for such a shirking. And it makes the blockchain vulnerable to the spreading of spam transactions over the network and creates extra burdens for all nodes in the network. We propose a reputation mechanism for the blockchain system to tackle this problem: Each node will locally compute reputations of its neighbors, and decide the probability to verify a received transaction based on the reputation value of the transaction sender. In turn, its neighbors will have an incentive to conduct verification to keep its reputation high. Subsequently, spam transactions can be blocked before reaching the miners. We have conducted a series of simulations, which clearly demonstrate the advantage of our reputation mechanism.

Motivated by the need for analyzing the rapidly producing data streams, such as images, videos, sensor data, etc, in a timely manner, the study on the streaming algorithms to extract representative information from massive data to maximize some objective function is therefore important and urgent. Most of previous works are assumed under a noise-free environment, while in many realistic applications obtaining the exact function value is hard or computing the function value may cost much, which brings the noisy version. Hence in this paper, we address a more general problem to select a subset of at most k elements from the stream to maximize a noisy set function (not necessarily submodular). To be specific, we cast our problem as the streaming submodular maximization problem under multiplicative and additive noise models. We develop an efficient thresholding streaming algorithm, which calls several copies of a subroutine in parallel. Therefore, this algorithm only requires two passes over data and has a memory independent of data size. For both of noisy models, its approximation guarantee approaches 2/k. In our numerical experiments, we extensively evaluate the effectiveness of our thresholding streaming algorithm on some applications in real data set.