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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. 

The Spectacles wearable smart glasses device from Snapchat records snaps and videos for the Snapchat service. A Spectacles device can sync data with a paired smartphone and upload recorded content to a user’s online account. However, extracting and analyzing data from a Snapchat app is challenging due to the disappearing nature of the media. Very few commercial tools are available to obtain data from Snapchat apps. This chapter focuses on the extraction and analysis of artifacts from Snapchat and, specifically, Spectacles devices paired with Apple iPhones. A methodology is presented for forensically imaging Apple iPhones before and after critical points in the Spectacles and Snapchat pairing and syncing processes. The forensic images are examined to reveal the effects of each step of the pairing process. Several photos, videos, thumbnails and metadata files originating from Spectacles devices were obtained and tied to specific times, devices and locations. The research provides interesting insights into evidence collection from Spectacles devices paired with Apple iPhones. 

The constant and rapid evolution of technology has led to some amazing achievements. Normal people can communicate with others across the globe, relatively cheap Internet of Things (IoT) devices can be used to secure homes, track fitness and health, control appliances, etc., many people have access to a seemingly endless wealth of information in small devices in their pockets, organizations can provide high availability for important services by spinning up/down servers in minutes to scale with demand through cloud services, etc. However, not everyone who uses these technologies does so with a pure heart and good intentions, many people use them to commit or help commit crimes. A nefarious individual may use cloud services to host a highly available Command and Control (C2) server, a messaging app to form and communicate with a gang or hacking group, or IoT devices as part of a botnet designed to perform Distributed Denial of Service (DDoS) attacks. When these technologies are used in the commission of a crime, they hold valuable information that needs to be recovered forensically to use as evidence to convict the perpetrators. Unfortunately, that ever-evolving technology poses many challenges for digital forensics. This paper identifies and presents many of the challenges faced in digital forensics involving mobile devices, IoT devices, and cloud services in addition to proposing a framework for solving the IoT Forensic Data Analysis problem.