Player authentication is important to ensure fairness in esports, and fighting games present a particularly challenging setting because of their discrete and limited input modalities and rapidly changing gameplay situations. While prior work has demonstrated the feasibility of controller-operation-based authentication in this domain, it has evaluated the approach only on a single title, leaving the empirical basis for this line of research still narrow. In this paper, using replay data from GUILTY GEAR -STRIVE-, we present a method for constructing a dataset for player authentication, which integrates three types of information: controller operation logs, battle progression, and battle attributes. To demonstrate its utility, we apply an existing controller-operation-based authentication method and show that the dataset supports the evaluation of such methods. We also analyze the relationship between player proficiency and authentication performance, and show that authentication tends to become more difficult as player proficiency increases. Our results show replay-derived data are practical for evaluating and analyzing player authentication in fighting games.

As one of the largest entertainment sectors globally, the gaming industry has already surpassed the music industry in revenue and is expected to continue its steady growth in the years ahead. Beyond leisure, gaming has become a competitive arena, particularly with the rise of esports, where games like CounterStrike (CS) draws millions of players and viewers worldwide. With the expansion of online gaming and virtual environments, new security challenges emerge that require advanced solutions. One promising approach is biometric identification based on ingame behavioral data, which is non-invasive and challenging to replicate. Although research has demonstrated effective player identification in Virtual Reality and turn-based games like chess, games like$C S$introduce unique challenges due to their dynamic high-speed interactions. This study explores behavioral biometrics to identify esports players based on their in-game behaviors. We use a multiclass Random Forest Classifier to analyze in-game movement, positioning, and weapon choices to identify individual players accurately. Our models achieve up to 98 % accuracy based on a combined dataset of 360 players across 320 matches. This data is divided into eight separate datasets for each map. These findings contribute to behavior-based player biometrics and identification, with applications in player analytics, team strategy optimization, and security in competitive gaming.

Summary This article presents a tracing mechanism for group signatures answering the security threats of malicious authorities and users' forgeries. The proposal weakens the high trust placed on the centralized tracing party in previous group signatures by decentralizing tracing power using a multiple tracer setting and limiting the tracers' access using attribute‐based encryption and the requirement of the group manager's agreement. We allow the group manager to control tracers identifying his group users. Instead of a centralized tracer, our setting has multiple tracers possessing attribute sets. Thus, after getting the group manager's permission, a tracer should satisfy the access policy in a given signature to identify the signer. On the other hand, our group signature scheme decentralizes the tracing key generation and removes the group manager's tracing ability. Thus, it ensures that only the attribute‐satisfying and permitted tracers can identify the signer. Moreover, this article delivers security against malicious users. It presents a verification process of access policy of the signatures to prevent users from utilizing invalid attributes for signing. In addition, the article delivers a collaborative tracing mechanism to satisfy attribute sets that a tracer fails to fulfill alone for identifying a signer. Thus, our tracing mechanism ensures security against malicious authorities and group users in group signatures. The article gives the general construction of the scheme and discusses the security.

We propose a decentralized but collaborative attribute-based tracing mechanism (a signer-identifying mechanism) for group signatures. Instead of a central tracing party in our scheme, a set of tracers satisfying the attribute set used for generating the group signature can identify the signer. Thus our proposal limits the parties who can identify the signer. On the other hand, it decentralized the tracing authority.

Driving to the research trend to the Internet of Vehicle (IoV), the issues of privacy and security of each internet car become popular. We focus on the certificate management to reduce the cost of certificate validation securely. In this paper, we use the blockchain technology to address the distribution and management of the Certificate Revocation List (CRL) in vehicle public key infrastructure (PKI). Our proposed scheme uses the activation codes to validate the certificate depends on time to non-revoked vehicle for blockchain mechanism. We intend to reduce the verification cost and naturally remove the certificate of inactive cars.

In PKC 2014, Langlois et al. proposed the first lattice-based group signature scheme with the verifier-local revocability. The security of their scheme is selfless anonymity, which is weaker than the security model defined by Bellare, Micciancio and Warinschi (EUROCRYPT 2003). By using the technique in the group signature scheme proposed by Ling et al. (PKC 2015), we propose a group signature scheme with the verifier-local revocability. For the security discussion of our scheme, we adapt the BMW03 model to cope with revocation queries, since the BMW03 model is for static groups. Then, we show that our scheme achieves the full anonymity in the adapted BMW03 model.