An official website of the United States government
Deep neural networks (DNNs) are being applied to various areas such as computer vision, autonomous vehicles, and healthcare, etc. However, DNNs are notorious for their high computational complexity and cannot be executed efficiently on resource constrained Internet of Things (IoT) devices. Various solutions have been proposed to handle the high computational complexity of DNNs. Offloading computing tasks of DNNs from IoT devices to cloud/edge servers is one of the most popular and promising solutions. While such remote DNN services provided by servers largely reduce computing tasks on IoT devices, it is challenging for IoT devices to inspect whether the quality of the service meets their service level objectives (SLO) or not. In this paper, we address this problem and propose a novel approach named QIS (quality inspection sampling) that can efficiently inspect the quality of the remote DNN services for IoT devices. To realize QIS, we design a new ID-generation method to generate data (IDs) that can identify the serving DNN models on edge servers. QIS inserts the IDs into the input data stream and implements sampling inspection on SLO violations. The experiment results show that the QIS approach can reliably inspect, with a nearly 100% success rate, the service qualtiy of remote DNN services when the SLA level is 99.9% or lower at the cost of only up to 0.5% overhead.
more »
« less
This content will become publicly available on November 27, 2025
CoVFeFE: Collusion-Resilient Verifiable Computing Framework for Resource-Constrained Devices at Network Edge
With the rapid growth of Internet of Vehicles (IoV) applications and the advancement of edge computing, resource-limited vehicles (and other IoT devices) increasingly rely on external servers to handle diverse and complex computational tasks. However, this dependence on external servers, which may be malicious or compromised, introduces significant security risks. Replication-based verifiable computing has been proposed as a solution to verify the accuracy of task results, but these approaches are vulnerable to collusion, where compromised servers return identical incorrect results to mislead the vehicle. Existing defenses against collusion either cannot ensure complete protection or become ineffective as the number of colluding servers rises. In this paper, we introduce CoVFeFE, a collusion-resilient verification framework designed to detect and mitigate collusion, even when the majority of servers are compromised. Our framework integrates a rapid detection mechanism that monitors computational conflicts, alongside a heuristic mitigation strategy that identifies and neutralizes colluding servers. Simulation results demonstrate that CoVFeFE outperforms existing solutions by successfully identifying all colluding servers, even when they constitute a majority > 50%) of the network.
more »
« less
- Award ID(s):
- 2148358
- PAR ID:
- 10585582
- Publisher / Repository:
- IEEE
- Date Published:
- ISSN:
- 2771-5663
- ISBN:
- 979-8-3503-7656-2
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Location:
- Rio de Janeiro, Brazil
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. However, data privacy becomes a major concern for vehicular edge computing, as sensitive sensor data from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles’ sensor data and the detection results. In this paper, we present vehicle–edge cooperative deep-learning networks with privacy protection for object-detection tasks, named vePOD for short. In vePOD, we leverage the additive secret sharing theory to develop secure functions for every layer in an object-detection convolutional neural network (CNN). A vehicle’s sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We have developed proof-of-concept detection networks with secure layers: vePOD Faster R-CNN (two-stage detection) and vePOD YOLO (single-stage detection). Experimental results on public datasets show that vePOD does not degrade the accuracy of object detection and, most importantly, it protects data privacy for vehicles. The execution of a vePOD object-detection network with secure layers is orders of magnitude faster than the existing approaches for data privacy. To the best of our knowledge, this is the first work that targets privacy protection in object-detection tasks with vehicle–edge cooperative computing.more » « less
-
Connected Autonomous Vehicles (CAVs) have achieved significant improvements in recent years. The CAVs can share sensor data to improve autonomous driving performance and enhance road safety. CAV architecture depends on roadside edge servers for latency-sensitive applications. The roadside edge servers are equipped with high-performance embedded edge computing devices that perform calculations with low power requirements. As the number of vehicles varies over different times of the day and vehicles can request for different CAV applications, the computation requirements for roadside edge computing platform can also vary. Hence, a framework for dynamic deployment of edge computing platforms can ensure CAV applications’ performance and proper usage of the devices. In this paper, we propose R-CAV – a framework for drone-based roadside edge server deployment that provides roadside units (RSUs) based on the computation requirement. Our proof of concept implementation for object detection algorithm using Nvidia Jetson nano demonstrates the proposed framework's feasibility. We posit that the framework will enhance the intelligent transport system vision by ensuring CAV applications’ quality of service.more » « less
-
null (Ed.)In the conventional robust T -colluding private information retrieval (PIR) system, the user needs to retrieve one of the possible messages while keeping the identity of the requested message private from any T colluding servers. Motivated by the possible heterogeneous privacy requirements for different messages, we consider the ( N,T1:K1,T2:K2 ) two-level PIR system, where K1 messages need to be retrieved privately against T1 colluding servers, and all the messages need to be retrieved privately against T2 colluding servers where T2≤T1 . We obtain a lower bound to the capacity by proposing a novel coding scheme, namely the non-uniform successive cancellation scheme. A capacity upper bound is also derived. The gap between the upper bound and the lower bound is analyzed, and shown to vanish when T1=T2 .more » « less
-
We present Whisper, a system for privacy-preserving collection of aggregate statistics. Like prior systems, a Whisper deployment consists of a small set of non-colluding servers; these servers compute aggregate statistics over data from a large number of users without learning the data of any individual user. Whisper's main contribution is that its server-to-server communication cost and its server-side storage costs scale sublinearly with the total number of users. In particular, prior systems required the servers to exchange a few bits of information to verify the well-formedness of each client submission. In contrast, Whisper uses silently verifiable proofs, a new type of proof system on secret-shared data that allows the servers to verify an arbitrarily large batch of proofs by exchanging a single 128-bit string. This improvement comes with increased client-to-server communication, which, in cloud computing, is typically cheaper (or even free) than the cost of egress for server-to-server communication. To reduce server storage, Whisper approximates certain statistics using small-space sketching data structures. Applying randomized sketches in an environment with adversarial clients requires a careful and novel security analysis. In a deployment with two servers and 100,000 clients of which 1% are malicious, Whisper can improve server-to-server communication for vector sum by three orders of magnitude while each client's communication increases by only 10%.more » « less
