CPU-FPGA heterogeneous architectures have become increasingly popular in cloud environments for accelerating compute-intensive tasks. Ensuring the protection of sensitive data processed by these architectures requires the presence of a trusted execution environment (TEE). This work highlights the requirements for designing an FPGA TEE, the challenges faced in deploying existing solutions on commercial-off-the-shelf (COTS) cloud FPGA services, and the limitations of previous works that primarily focus on standalone FPGA TEEs. In response to these challenges, Salus introduces an innovative approach by leveraging an enclave running on the host with a TEE-enabled CPU. This approach aims to protect and attest the bitstream loaded on the FPGA side. By repurposing COTS FPGA bitstream utilities in a novel manner and adopting a proposed security-enhanced FPGA IP, Salus presents a practical design for an FPGA TEE, with minor efforts required.

This paper proposes a methodology for radial medium-voltage distribution networks topology modeling with the voltage-power sensitivity based on smart meters data. Having different characteristic, partial least squares regression (PLS) and deep neural network (DNN) are respectively used to obtain the sensitivity. The relationship between the sensitivity and the topological position is analyzed, based on which, the topology is identified. The impendence of each line is estimated accordingly by the voltage value and the sensitivity obtained by DNN. The methods presented in this paper do not necessitate any prior topological information or additional data apart from voltage and power readings collected by smart meters. Their efficacy is initially evaluated using the IEEE-33 test case, followed by assessment across various states of the IEEE-69, demonstrating remarkable efficiency and accuracy.

Palmprint recognition has shown great value for biometric recognition due to its advantages of good hygiene, semi-privacy and low invasiveness. However, most existing palmprint recognition studies focus only on homogeneous palmprint recognition, where comparing palmprint images are collected under similar conditions with small domain gaps. To address the problem of matching heterogeneous palmprint images captured under the visible light (VIS) and the near-infrared (NIR) spectrum with large domain gaps, in this paper, we propose a Fourier-based feature learning network (FFLNet) for VIS-NIR heterogeneous palmprint recognition. First, we extract the multi-scale shallow representations of heterogeneous palmprint images via three vanilla convolution layers. Then, we convert the shallow palmprint feature maps into frequency-specific representations via Fourier transform to separate different layers of palmprint features, and exploit the underlying common and palmprint-specific frequency information of heterogeneous palmprint images. This effectively reduces the modality gap of heterogeneous palmprint images at the feature level. After that, we convert the common frequency-specific feature maps back to the spatial domain to learn the identity-invariant discriminative features via residual convolution for heterogeneous palmprint recognition. Extensive experimental results on three challenging heterogeneous palmprint databases clearly demonstrate the effectiveness of the proposed FFLNet for VIS-NIR heterogeneous palmprint recognition.

Unconstrained palmprint images have shown great potential for recognition applications due to their lower restrictions regarding hand poses and backgrounds during contactless image acquisition. However, they face two challenges: 1) unclear palm contours and finger-valley points of unconstrained palmprint images make it difficult to locate landmarks to crop the palmprint region of interest (ROI); and 2) large intra-class diversities of unconstrained palmprint images hinder the learning of intra-class-invariant palmprint features. In this paper, we propose to directly extract the complete palmprint region as the ROI (CROI) using the detection-style CenterNet without requiring the detection of any landmarks, and large intra-class diversities may occur. To address this, we further propose a palmprint feature alignment and learning hybrid network (PalmALNet) for unconstrained palmprint recognition. Specifically, we first exploit and align the multi-scale shallow representation of unconstrained palmprint images via deformable convolution and alignment-aware supervision, such that the pixel gaps of the intra-class palmprint CROIs can be minimized in shallow feature space. Then, we develop multiple triple-attention learning modules by integrating spatial, channel, and self-attention operations into convolution to adaptively learn and highlight the latent identity-invariant palmprint information, enhancing the overall discriminative power of the palmprint features. Extensive experimental results on four challenging palmprint databases demonstrate the promising effectiveness of both the proposed PalmALNet and CROI for unconstrained palmprint recognition.

Recent years have witnessed the rapid development of the encrypted database, due to the increasing number of data privacy breaches and the corresponding laws and regulations that caused millions of dollars in loss. These encrypted databases may rely on different techniques, such as cryptographic primitives and trusted execution environments. In this work, we investigate the feasibility of utilizing fully homomorphic encryption (FHE) to support unbounded database aggregation queries, which typically involve comparisons as filtering predicates and a final aggregation. These operators are theoretically supported by FHE, but need careful algorithm design to maximize the efficiency and have not been explored before. We creatively use two types of FHE schemes, i.e. , one for numerical and one for binary value, to enjoy their advantages respectively. To bridge the encrypted values between these two schemes for seamless query processing without client-server interaction, we propose a novel ciphertext transformation mechanism, which is of independent research interest, to close this gap. We further implement our system and test it over three TPC-H queries and a query over a real social media e-commerce database. Evaluation results show that, to process an aggregation query over 8 k encrypted rows takes about 430 seconds. Although it is slower than plaintext processing in magnitudes and still has much room for improvement, as the very first work in this domain, our system demonstrates the feasibility of using FHE to process OLAP queries.

The past decade has witnessed the rapid development of cloud computing and data-centric applications. While these innovations offer numerous attractive features for data processing, they also bring in new issues about the loss of data ownership. Though some encrypted databases have emerged recently, they can not fully address these concerns for the data owner. In this paper, we propose an ownership-preserving database (OPDB), a new paradigm that characterizes different roles' responsibilities from nowadays applications and preserves data ownership throughout the entire application. We build Operon to follow the OPDB paradigm, which utilizes the trusted execution environment (TEE) and introduces a behavior control list (BCL). Different from access controls that merely handle accessibility permissions, BCL further makes data operation behaviors under control. Besides, we make Operon practical for real-world applications, by extending database capabilities towards flexibility, functionality and ease of use. Operon is the first database framework with which the data owner exclusively controls its data across different roles' subsystems. We have successfully integrated Operon with different TEEs, i.e. , Intel SGX and an FPGA-based implementation, and various database services on Alibaba Cloud, i.e. , PolarDB and RDS PostgreSQL. The evaluation shows that Operon achieves 71% - 97% of the performance of plaintext databases under the TPC-C benchmark while preserving the data ownership.

Vehicular sensing is advocated to perform data collection by exploiting a plethora of vehicular on-board sensors; meanwhile, with the merging of vehicular sensing and fog computing, the deployed road side units (RSUs) can act as fog nodes to collect and share vehicular sensory data at the network edge. However, there are still several problems in terms of the secure and reliable sharing of sensory data in vehicular fog. To resolve these issues, in this paper, we present an efficient, privacy-preserving and verifiable sensory data collection and sharing scheme with a permissioned blockchain in vehicular fog. During the data collection phase, by combining the homomorphic 2-DNF (Disjunctive Normal Form) cryptosystem and an identity-based signcryption scheme, our proposed scheme achieves the secure and verifiable computation of the average and variance of the collected vehicular sensory data. Meanwhile, to achieve efficient and reliable data sharing, we exploit a permissioned blockchain to maintain an immutable and tamper-proof record of the derived sensory data. Security analysis demonstrates the security properties of the proposed scheme, in terms of location privacy preservation, verifiability and immutability. Performance evaluations are conducted to validate the efficiency of the proposed scheme, i.e., improvements in computation and communication efficiency in comparison with a scheme without exploiting blockchain.

The ubiquitous deployment of Internet of Things (IoT) devices enhances connectivity and communication, and benefits almost every aspect of our lives from manufacturing to retail to smart homes. However, low levels of security protection in these devices due to their limited resources open opportunities for malicious users. An IoT forensics system collecting, processing, analyzing and reporting evidence of attack is required to mitigate the IoT security issues. Although such system has been studied over the past decade and solutions such as cloud-based IoT forensic were proposed, limitation still exist. In this paper, leveraging on the blockchain technology, we propose a per-missioned blockchain-based IoT forensics framework to enhance the integrity, authenticity and non-repudiation properties for the collected evidence. We formally define the system architecture, provide framework details, and propose a cryptographic-based approach to mitigate identity privacy concern.

Connected machines become more vulnerable to malware infections which potentially cause them to be controlled as part of a botnet for cybercrime activities. Prompt detection of infected machines is required for protecting local networks and infrastructure as well as reducing the impact of botnets. In this paper, we propose the use of machine learning techniques involving multi-layer perceptrons and decision trees on network traffic analysis for the detection of botnet traffic. We enhance components of an existing detection framework with these techniques to automate its processes and improve performance at the same time. Our experiments indicate that the modifications successfully improved the overall performance of botnet traffic detection in both supervised and semi-supervised manners.

There are increasing real-time live applications in virtual reality, where it plays an important role in capturing and retargetting 3D human pose. But it is still challenging to estimate accurate 3D pose from consumer imaging devices such as depth camera. This paper presents a novel cascaded 3D full-body pose regression method to estimate accurate pose from a single depth image at 100 fps. The key idea is to train cascaded regressors based on Gradient Boosting algorithm from pre-recorded human motion capture database. By incorporating hierarchical kinematics model of human pose into the learning procedure, we can directly estimate accurate 3D joint angles instead of joint positions. The biggest advantage of this model is that the bone length can be preserved during the whole 3D pose estimation procedure, which leads to more effective features and higher pose estimation accuracy. Our method can be used as an initialization procedure when combining with tracking methods. We demonstrate the power of our method on a wide range of synthesized human motion data from CMU mocap database, Human3.6M dataset and real human movements data captured in real time. In our comparison against previous 3D pose estimation methods and commercial system such as Kinect 2017, we achieve the state-of-the-art accuracy.

Anomaly detection in communication networks is the first step in the challenging task of securing a network, as anomalies may indicate suspicious behaviors, attacks, network malfunctions, or failures. In this paper, we address the problem of not only detecting the anomalous events but also of attributing the anomaly to the flows causing it. To this end, we develop a new statistical decision theoretic framework for temporally correlated traffic in networks via Markov chain modeling. We first formulate the optimal anomaly detection problem via the generalized likelihood ratio test (GLRT) for our composite model. This results in a combinatorial optimization problem which is prohibitively expensive. We then develop two low-complexity anomaly detection algorithms. The first is based on the cross entropy (CE) method, which detects anomalies as well as attributes anomalies to flows. The second algorithm performs anomaly detection via GLRT on the aggregated flows transformation - a compact low-dimensional representation of the raw traffic flows. The two algorithms complement each other and allow the network operator to first activate the flow aggregation algorithm in order to quickly detect anomalies in the system. Once an anomaly has been detected, the operator can further investigate which specific flows are anomalous by running the CE-based algorithm. We perform extensive performance evaluations and experiment our algorithms on synthetic and semi-synthetic data, as well as on real Internet traffic data obtained from the MAWI archive, and finally make recommendations regarding their usability.

Self-Organizing Networks (SON) is an automation technology making the planning, deployment, operation, optimization, and healing of networks simpler and faster. Legacy SON is targeted at network automation and network optimization through certain optimization rules and policies which are globally applied in networks. However, scalable and targeted optimization is not considered yet in 3GPP. Furthermore, SON is driven by performance optimization rather than ultimately improving user Quality of Experience (QoE). The impact of application characteristics on network performance and further on QoE are also not considered in 3GPP SON. This paper presents an application characteristics-driven SON system (APP-SON) to optimize 4G/5G network performance and user Quality of Experience. APP-SON leverages a scalable big data platform for targeted optimization through profiling cell application characteristics in an incremental manner in temporal space. A Hungarian Algorithm Assisted Clustering (HAAC) algorithm and a deep learning-assisted regression algorithm are developed to profile the cell application characteristics and find the targeted KPIs to be optimized for each cell. A similarity-based, parametertuning algorithm is developed to tune the corresponding engineering parameters to optimize the targeted KPIs which further improve QoE. Experimental results demonstrate that the APP-SON system can precisely profile cell traffic and application characteristics to find the targeted KPIs for optimization for each cell. APP-SON can also automatically tune the corresponding engineering parameters to improve the corresponding KPIs, ultimately improving QoE. APP-SON has been successfully implemented in production and applied in a tier-1 operator's 4G network and as a universal SON solution it will be smoothly transitioned and applied in 5G networks for this operator.

Marker labeling plays an important role in optical motion capture pipeline especially in real-time applications; however, the accuracy of online marker labeling is still unclear. This paper presents a novel accurate real-time online marker labeling algorithm for simultaneously dealing with missing and ghost markers. We first introduce a soft graph matching model that automatically labels the markers by using Hungarian algorithm for finding the global optimal matching. The key idea is to formulate the problem in a combinatorial optimization framework. The objective function minimizes the matching cost, which simultaneously measures the difference of markers in the model and data graphs as well as their local geometrical structures consisting of edge constraints. To achieve high subsequent marker labeling accuracy, which may be influenced by limb occlusions or self-occlusions, we also propose an online high-quality full-body pose reconstruction process to estimate the positions of missing markers. We demonstrate the power of our approach by capturing a wide range of human movements and achieve the state-of-the-art accuracy by comparing against alternative methods and commercial system like VICON.