相关论文: Ransomware Negotiation: Dynamics and Privacy-Prese…

Effective game-theoretic modeling of defender-attacker behavior is becoming increasingly important. In many domains, the defender functions not only as a player but also the designer of the game's payoff structure. We study Stackelberg…

We study a bilateral trade problem where a principal has private information that is revealed with delay, such as a seller who does not yet know her production cost. Postponing the contracting process incurs a costly delay, while early…

Membership inference attacks (MIAs) pose significant privacy risks by determining whether individual data is in a dataset. While differential privacy (DP) mitigates these risks, it has limitations including limited resolution in expressing…

The LockBit 3.0 ransomware variant is arguably the most threatening of malware in recent times. With no regard for a victim's industry, the ransomware has undergone several evolutions to arrive at an adaptable and evasive variant which has…

Ransomware defense solutions that can quickly detect and classify different ransomware classes to formulate rapid response plans have been in high demand in recent years. Though the applicability of adopting deep learning techniques to…

Understanding an opponent agent helps in negotiating with it. Existing works on understanding opponents focus on preference modeling (or estimating the opponent's utility function). An important but largely unexplored direction is…

We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that are rigorously justified, while also allowing for the…

We study a security game over a network played between a $defender$ and $k$ $attackers$. Every attacker chooses, probabilistically, a node of the network to damage. The defender chooses, probabilistically as well, a connected induced…

Data collecting agents in large networks, such as the electric power system, need to share information (measurements) for estimating the system state in a distributed manner. However, privacy concerns may limit or prevent this exchange…

Can we make a denial-of-service attacker pay more than the server and honest clients? Consider a model where a server sees a stream of jobs sent by either honest clients or an adversary. The server sets a price for servicing each job with…

Deception plays a key role in adversarial or strategic interactions for the purpose of self-defence and survival. This paper introduces a general framework and solution to address deception. Most existing approaches for deception consider…

Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc . Given the enormous social impact and the consequent incentives, the potential adversary has a strong motivation to…

In a multi-party machine learning system, different parties cooperate on optimizing towards better models by sharing data in a privacy-preserving way. A major challenge in learning is the incentive issue. For example, if there is…

Firms in inter-organizational networks such as supply chains or strategic alliances are exposed to interdependent risks. These are risks that are transferable across partner firms. They can be decomposed into intrinsic risks a firm faces…

Ransomware presents a significant and increasing threat to individuals and organizations by encrypting their systems and not releasing them until a large fee has been extracted. To bolster preparedness against potential attacks,…

Deception is a technique to mislead human or computer systems by manipulating beliefs and information. For the applications of cyber deception, non-cooperative games become a natural choice of models to capture the adversarial interactions…

Markov decision processes often seek to maximize a reward function, but onlookers may infer reward functions by observing the states and actions of such systems, revealing sensitive information. Therefore, in this paper we introduce and…

Online platforms in the Internet Economy commonly incorporate recommender systems that recommend products (or "arms") to users (or "agents"). A key challenge in this domain arises from myopic agents who are naturally incentivized to exploit…

Ransomware has remained one of the most notorious threats in the cybersecurity field. Moving Target Defense (MTD) has been proposed as a novel paradigm for proactive defense. Although various approaches leverage MTD, few of them rely on the…

The guarantees of security and privacy defenses are often strengthened by relaxing the assumptions made about attackers or the context in which defenses are deployed. Such relaxations can be a highly worthwhile topic of exploration---even…