Security games with malicious adversaries in the clouds: status update

Abstract

Outsourcing computational tasks to the cloud offers numerous advantages, such as availability, scalability, and elasticity. These advantages are evident when outsourcing resource-demanding Machine Learning (ML) applications. However, cloud computing presents security challenges. For instance, allocating Virtual Machines (VMs) with varying security levels onto commonly shared servers creates cybersecurity and privacy risks. Researchers proposed several cryptographic methods to protect privacy, such as Multi-party Computation (MPC). Attackers unfortunately can still gain unauthorized access to users' data if they successfully compromise a specific number of the participating MPC nodes. Cloud Service Providers (CSPs) can mitigate the risk of such attacks by distributing the MPC protocol over VMs allocated to separate physical servers (i.e., hypervisors). On the other hand, underutilizing cloud servers increases operational and resource costs, and worsens the overhead of MPC protocols. In this ongoing work, we address the security, communication and computation overheads, and performance limitations of MPC. We model this multi-objective optimization problem using several approaches, including but not limited to, zero-sum and non-zero-sum games. For example, we investigate Nash Equilibrium (NE) allocation strategies that reduce potential security risks, while minimizing response time and performance overhead, and/or maximizing resource usage.