Abstract: Digital societies increasingly rely on secure communication between parties. Certificate enrollment protocols are used by certificate authorities to issue public key certificates to clients. Key agreement protocols, such as Diffie-Hellman, are used to compute secret keys, using public keys as input, for establishing secure communication channels. Whenever the keys are generated by clients, the bootstrap process requires either (a) an out-of-band verification for certification of keys when those are generated by the clients themselves, or (b) a trusted server to generate both the public and secret parameters. This paper presents a novel constrained key agreement protocol, built upon a constrained Diffie-Hellman, which is used to generate a secure public-private key pair, and to set up a certification environment without disclosing the private keys. In this way, the servers can guarantee that the generated key parameters are safe, and the clients do not disclose any secret information to the servers.

Citation: F{\’a}bio Borges and Leonardo A. Martucci and Filipe Beato and and Max M{\”u}hlh{\”a}user (2014). Secure and Privacy-Friendly Public Key Generation and Certification. In Proceedings of the 13th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 24–26 September, Beijing, China, TrustCom 2014.

Download: http://bit.ly/2j4irD0

This work was presented at HAIDM 2014. The 2014 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: This paper proposes the Multi-Operation Patrol Scheduling System (MOPSS), a new system to generate patrols for transit system. MOPSS is based on five contributions. First, MOPSS is the first system to use three fundamentally different adversary models for the threats of fare evasion, terrorism and crime, generating three significantly different types of patrol schedule. Second, to handle uncertain interruptions in the execution of patrol schedules, MOPSS uses Markov decision processes (MDPs) in its scheduling. Third, MOPSS is the first system to account for joint activities between multiple resources, by employing the well known SMART security game model that tackles coordination between defender’s resources. Fourth, we are also the first to deploy a new Opportunistic Security Game model, where the adversary, a criminal, makes opportunistic decisions on when and where to commit crimes. Our fifth, and most important, contribution is the evaluation of MOPSS via real-world deployments, providing data from security games in the field.

Keywords: Security, Game-theory, Real-world deployment.

Citation: Francesco Delle Fave, Matthew Brown, Chao Zhang, Eric Shieh, Albert Jiang and Milind Tambe. Security Games in the Field: Deployments on a Transit System.

Download: http://bit.ly/1TcVx85

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: Recently, there has been an increase of interest in domains involving repeated interactions between defenders and adversaries. This has been modeled as a repeated Stackelberg Security Game (repeated SSG). Although different behavioral models have been proposed for the attackers in these games, human subjects experiments for testing these behavioral models in repeated SSGs have not been conducted previously. This paper presents the first “longitudinal study” – at least in the context of SSGs – of testing human behavior models in repeated SSG settings. We provide the following contributions in this paper. First, in order to test the behavioral models, we design a game that simulates the repeated interactions between the defender and the adversary and deploy it on Amazon Mechanical Turk (AMT). Human subjects are asked to participate in this repeated task in rounds of the game, with a break between consecutive rounds. Second, we develop several approaches to keep the human subjects motivated throughout the course of this longitudinal study so that they participate in all measurement occasions, thereby minimizing attrition. We provide results showing improvements of retention rate due to implementation of these approaches. Third, we propose a way of choosing representative payoffs that fit the real-world scenarios as conducting these experiments are extremely time-consuming and we can only conduct a limited number of such experiments.

Keywords: Game Theory, Human Behavior Models, Repeated Stackelberg Games, Longitudinal Experiments, Amazon Mechanical Turk.

Citation: Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov and Milind Tambe. Conducting Longitudinal Experiments with Behavioral Models in Repeated Stackelberg Security Games on Amazon Mechanical Turk.

Download: http://bit.ly/1rf3wsu

Social computing broadly refers to computing-supported approaches that facilitate interactions among people and organizations. Social computing has emerged as an exciting multidisciplinary area of research, driven by the wealth of easily available information and the success of online social networks and social media. Social computing applications are characterized by high interactivity among users, user-generated content, and in cases such as Wikipedia, more open governance structures. Much of the recent excitement in social computing is driven by data analytics and business models. What is still lacking, however, is a deeper conceptual understanding of social computing — e.g., relating to its conceptual bases, information and abstractions, design principles, and platforms. This event invites papers that take an explicitly multiagent perspective in addressing these gaps and do so in thought-provoking ways.

The First International Workshop on Multiagent Foundations of Social Computing is Co-located with AAMAS 2014

Important Dates

• Submission: January 22, 2014
• Notification: February 19, 2014
• Camera-ready due: March 5, 2014