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Pervasive Edge Computing (PEC), a recent addition to the edge computing paradigm, leverages the computing resources of end-user devices to execute computation tasks in close proximity to users. One of the primary challenges in the PEC environment is determining the appropriate servers for offloading computation tasks based on factors, such as computation latency, response quality, device reliability, and cost of service. Computation outsourcing in the PEC ecosystem requires additional security and privacy considerations. Finally, mechanisms need to be in place to guarantee fair payment for the executed service(s). We present 𝑃𝐸𝑃𝑃𝐸𝑅, a novel, privacy-preserving, and decentralized framework that addresses aforementioned challenges by utilizing blockchain technology and trusted execution environments (TEE). 𝑃𝐸𝑃𝑃𝐸𝑅 improves the performance of PEC by allocating resources among end-users efficiently and securely. It also provides the underpinnings for building a financial ecosystem at the pervasive edge. To evaluate the effectiveness of 𝑃𝐸𝑃𝑃𝐸𝑅, we developed and deployed a proof of concept implementation on the Ethereum blockchain, utilizing Intel SGX as the TEE technology. We propose a simple but highly effective remote attestation method that is particularly beneficial to PEC compared to the standard remote attestation method used today. Our extensive comparison experiment shows that 𝑃𝐸𝑃𝑃𝐸𝑅 is 1.23× to 2.15× faster than the current standard remote attestation procedure. In addition, we formally prove the security of our system using the universal composability (UC) framework. 

The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privacy. However, in some applications, such as Augmented/Virtual Reality, integrity verification of the outsourced ML tasks is more critical–a face that has not received much attention. Existing solutions, such as multi-party computation and proof-based systems, impose significant computation overhead, which makes them unfit for real-time applications. We propose Fides, a novel framework for real-time integrity validation of ML-as-a-Service (MLaaS) inference. Fides features a novel and efficient distillation technique–Greedy Distillation Transfer Learning–that dynamically distills and fine-tunes a space and compute-efficient verification model for verifying the corresponding service model while running inside a trusted execution environment. Fides features a client-side attack detection model that uses statistical analysis and divergence measurements to identify, with a high likelihood, if the service model is under attack. Fides also offers a re-classification functionality that predicts the original class whenever an attack is identified. We devised a generative adversarial network framework for training the attack detection and re-classification models. The evaluation shows that Fides achieves an accuracy of up to 98% for attack detection and 94% for re-classification. 

New breed of applications, such as autonomous driving and their need for computation-aided quick decision making has motivated the delegation of compute-intensive services (e.g., video analytic) to the more powerful surrogate machines at the network edge–edge computing (EC). Recently, the notion of pervasive edge computing (PEC) has emerged, in which users’ devices can join the pool of the computing resources that perform edge computing. Inclusion of users’ devices increases the computing capability at the edge (adding to the infrastructure servers), but in comparison to the conventional edge ecosystems, it also introduces new challenges, such as service orchestration (i.e., service placement, discovery, and migration). We propose uDiscover, a novel user-driven service discovery and utilization framework for the PEC ecosystem. In designing uDiscover, we considered the Named-Data Networking architecture for balancing users workloads and reducing user-perceived latency. We propose proactive and reactive service discovery approaches and assess their performance in PEC and infrastructure-only ecosystems. Our simulation results show that (i) the PEC ecosystem reduces the user-perceived delays by up to 70%, and (ii) uDiscover selects the most suitable server–"accurate" delay estimates with less than 10% error–to execute any given task.