Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size.

The unit of federated learning and differential privacy protection technology can ensure that the published aggregation has sufficient anonymity. While realizing privacy protection, users' data can be fully utilized. However, attackers may still steal user information by eavesdropping on the shared model of federated learning participants. In this paper, we mainly study the multi-party collaborative privacy protection mechanism based on federated learning. Firstly, we design a stochastic forest algorithm satisfying differential privacy in a distributed computing framework. The algorithm first segments the nodes in the weak classifier into the best segmentation points, and then perturbs the leaf nodes through the Laplace mechanism to protect the classification results. Secondly, we present a differential privacy-preserving mechanism based on XGBoost, aiming at the privacy leakage problem that may occur during the construction of weak classifiers. Finally, we propose a multi-party collaborative construction scheme based on federated learning to meet differential privacy. At last, we empirically confirm that our algorithm achieves state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL. Moreover, numerous experimental results indicate that the proposed framework can ensure data security and also have high accuracy.

In order to choose from a list of functionally similar services, users often need to make their decisions based on multiple QoS criteria they require on the target service. In this process, different users may follow different decision making strategies, some are compensatory in which only an overall value on all the criteria is evaluated, some evaluate one criterion at a time in the order of their importance levels, while others count on the number of winning criteria. Most of the current QoS-based service selection systems do not consider these decision strategies in the ranking process, which we believe are crucial for generating accurate ranking results for individual users. In this paper, we propose a decision strategy based service ranking model. Furthermore, considering that different users follow different strategies in different contexts at different times, we apply a machine learning algorithm to learn a personalized ranking model for individual users based on how they select services in the past. We have implemented and tested the proposed approach, and our experiment results show the effectiveness of the approach.

The detection of image tampering, specifically copy detection, is an important problem in many domains such as military, media, and public opinion outlets. Effective means to detect such tampering is crucial in controlling the dissemination of false information. However, a major challenge in achieving high detection accuracy lies in the variability of the scale of the copied targets. To tackle this problem, we introduce an all-encompassing methodology called Cross-Scale Modeling and Alternating Refinement (CANet) to detect the genuine source and tampered region at the pixel level. CANet consists of three modules: the Cross-Scale Similar Region Detection (CS) module, the Edge-Supervised Tamper Region Detection (ET) module, and the Alternating Refinement (AR) module. The CS module extracts coarse similar region features by cross-scale correlation modeling, which can alleviate the scale gap between the source and tampered region. The obtained coarse similar region feature is refined by the AR module, in which we introduce the source and the tampered region as the auxiliary information and employ a two-stage process that sequentially models their global feature representations. The tampered region used in the AR module is obtained from the ET module using edge supervision with a salient edge selection scheme, and the source region is generated by the implicit modeling. We conducted experiments on the USC-ISI, CASIA v2.0, CoMoFoD, and MICC-F220 datasets separately. Results show that our method outperforms the state-of-the-art.

Unmanned aerial vehicles (UAVs) equipped with high line-of-sight communications have been explored as a complement to emergency communication vehicles (ECVs), particularly when ground Internet of Vehicles (IoVs) is dysfunctional resulting from natural disasters. To protect the communication security and integrity of this air-ground integrated networks created in an untrusted and open wireless environment, authentication and key agreement (AKA) is an essential mechanism to establish a secure communication channel by negotiating a session key. Nonetheless, general AKA protocols are typically not ideal for time-sensitive and computing-intensive UAVs-assisted IoVs emergency rescue, in that they are unable to simultaneously satisfy efficiency and key security requirements including accountability, resilience, thresholds changeability, shares verifiability, group adaptability, and key update. To address this challenge, this paper proposes an accountable group authentication and key agreement (GAKA) protocol with changeable thresholds and verifiable shares supporting adaptive group memberships and updatable keys (i.e. SecER). To safeguard accurate rescue decisions for ground ECVs enabled by reliable collaboration among multiple UAVs, we propose a pseudonym mechanism that aims to provide accountability for UAVs. To achieve resilience and thresholds changeability, our SecER utilizes secret sharing coupled with random parameters to seamlessly transform our GAKA protocol into one running with a new threshold in one round (i.e. round-optimal), rather than two rounds. Finally, verifiable parameters and updatable keys are respectively applied to counter deception attacks caused by maliciously distributed shares and to support adaptive network topology (i.e. UAVs joining and leaving). Extensive simulations show that compared to the state-of-the-art approaches, our SecER is superior in balancing security and efficiency.