5,789 research outputs found
Differential Privacy in Cooperative Multiagent Planning
Privacy-aware multiagent systems must protect agents' sensitive data while
simultaneously ensuring that agents accomplish their shared objectives. Towards
this goal, we propose a framework to privatize inter-agent communications in
cooperative multiagent decision-making problems. We study sequential
decision-making problems formulated as cooperative Markov games with
reach-avoid objectives. We apply a differential privacy mechanism to privatize
agents' communicated symbolic state trajectories, and then we analyze tradeoffs
between the strength of privacy and the team's performance. For a given level
of privacy, this tradeoff is shown to depend critically upon the total
correlation among agents' state-action processes. We synthesize policies that
are robust to privacy by reducing the value of the total correlation. Numerical
experiments demonstrate that the team's performance under these policies
decreases by only 3 percent when comparing private versus non-private
implementations of communication. By contrast, the team's performance decreases
by roughly 86 percent when using baseline policies that ignore total
correlation and only optimize team performance
Bargaining Mechanisms for One-Way Games
We introduce one-way games, a framework motivated by applications in
large-scale power restoration, humanitarian logistics, and integrated
supply-chains. The distinguishable feature of the games is that the payoff of
some player is determined only by her own strategy and does not depend on
actions taken by other players. We show that the equilibrium outcome in one-way
games without payments and the social cost of any ex-post efficient mechanism,
can be far from the optimum. We also show that it is impossible to design a
Bayes-Nash incentive-compatible mechanism for one-way games that is
budget-balanced, individually rational, and efficient. To address this negative
result, we propose a privacy-preserving mechanism that is incentive-compatible
and budget-balanced, satisfies ex-post individual rationality conditions, and
produces an outcome which is more efficient than the equilibrium without
payments. The mechanism is based on a single-offer bargaining and we show that
a randomized multi-offer extension brings no additional benefit.Comment: An earlier, shorter version of this paper appeared in Proceedings of
the Twenty-Fourth International joint conference on Artificial Intelligence
(IJCAI) 201
How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed
A Privacy-Aware Access Control Model for Distributed Network Monitoring
International audienceIn this paper, we introduce a new access control model that aims at addressing the privacy implications surrounding network monitoring. In fact, despite its importance, network monitoring is natively leakage-prone and, moreover, this is exacerbated due to the complexity of the highly dynamic monitoring procedures and infrastructures, that may include multiple traffic observation points, distributed mitigation mechanisms and even inter-operator cooperation. Conceived on the basis of data protection legislation, the proposed approach is grounded on a rich in expressiveness information model, that captures all the underlying monitoring concepts along with their associations. The model enables the specification of contextual authorisation policies and expressive separation and binding of duty constraints. Finally, two key innovations of our work consist in the ability to define access control rules at any level of abstraction and in enabling a verification procedure, which results in inherently privacy-aware workflows, thus fostering the realisation of the Privacy by Design vision
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