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Poster: MLess: Deep Learning Application Platform for Smart Cities 24

Poster: MLess: Deep Learning Application Platform for Smart ...

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22nd Annual international conference on Mobile systems, Applications and Services (MOBISYS)

作者： Makino, Seiki Okoshi, Tadashi Nakazawa, Jin Keio Univ Fujisawa Kanagawa Japan

ISBN: (纸本)9798400705816

Many smart city applications utilize deep learning technologies to process the data generated by sensors and smart devices. However, current application hosting platforms are not suitable for deep learning applications, because of their special requirements, including GPU and large trained model data. We propose a smart city application hosting platform named MLess, which serves and scales deep-learning applications across servers. MLess adds an extra abstraction layer between applications and executing hosts, thus it allows developers to write applications in a serverless manner. We developed a Proof-of-Concept implementation of MLess and made preliminary evaluations against it. In future work, we plan to add QoS support like inference accuracy or throughput.

关键词： Smart Cities Serverless computing Deep Learning

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Actionable Contextual Explanations for Cyber-Physical systems 22

Actionable Contextual Explanations for Cyber-Physical System...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Jha, Sanjiv S. Mayer, Simon Garcia, Kimberly Univ St Gallen Inst Comp Sci St Gallen Switzerland

ISBN: (纸本)9798350381993;9798350382006

Over the past two decades, Cyber-Physical systems (CPS) have emerged as critical components in various industries, integrating digital and physical elements to improve efficiency and automation, from smart manufacturing and autonomous vehicles to advanced healthcare devices. However, the increasing complexity of CPS and their deployment in highly dynamic contexts undermine user trust. This motivates the investigation of methods capable of generating explanations about the behavior of CPS. To this end, Explainable Artificial Intelligence (XAI) methodologies show potential. However, these approaches do not consider contextual variables that a CPS may be subjected to (e.g., temperature, humidity), and the provided explanations are typically not actionable. In this article, we propose an Actionable Contextual Explanation System (ACES) that considers such contextual influences. Based on a user query about a behavioral attribute of a CPS (for example, vibrations and speed), ACES creates contextual explanations for the behavior of such a CPS considering its context. To generate contextual explanations, ACES uses a context model to discover sensors and actuators in the physical environment of a CPS and obtains time-series data from these devices. It then cross-correlates these time-series logs with the user-specified behavioral attribute of the CPS. Finally, ACES employs a counterfactual explanation method and takes user feedback to identify causal relationships between the contextual variables and the behavior of the CPS. We demonstrate our approach with a synthetic use case;the favorable results obtained, motivate the future deployment of ACES in real-world scenarios.

关键词： explainability cyber-physical systems knowledge graph reasoning context XAI

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A First Look at Digital Rights Management systems for Secure Mobile Content Delivery 22

A First Look at Digital Rights Management Systems for Secure...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Rafi, Amir Shepherd, Carlton Markantonakis, Konstantinos Royal Holloway Univ London Informat Secur Grp Egham Surrey England Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9798350381993;9798350382006

Digital rights management (DRM) solutions aim to prevent the copying or distribution of copyrighted material. On mobile devices, a variety of DRM technologies have become widely deployed. However, a detailed security study comparing their internal workings, and their strengths and weaknesses, remains missing in the existing literature. In this paper, we present the first detailed security analysis of mobile DRM systems, addressing the modern paradigm of cloud-based content delivery followed by major platforms, such as Netflix, Disney+, and Amazon Prime. We extensively analyse the security of three widely used DRM solutions-Google Widevine, Apple FairPlay, and Microsoft PlayReady-deployed on billions of devices worldwide. We then consolidate their features and capabilities, deriving common features and security properties for their evaluation. Furthermore, we identify some design-level shortcomings that render them vulnerable to emerging attacks within the state of the art, including micro-architectural side-channel vulnerabilities and an absence of post-quantum security. Lastly, we propose mitigations and suggest future directions of research.

关键词： Digital rights management (DRM) media streaming trusted execution environments mobile security

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Quantum Predator -Prey Brain Storm Optimization for Robust Optimal Allocation of D-STATCOM Devices 22

Quantum Predator -Prey Brain Storm Optimization for Robust O...

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22nd international conference on Intelligent systems Applications to Power systems

作者： Kawauchi, Yusuke Someya, Naohisa Mori, Hiroyuki Chiang, Hsiao-Dong Meiji Univ Dept Network Design Tokyo Japan J Power Transmiss Network Co Ltd Hashimoto Branch Hashimoto Wakayama Japan Cornel Univ Dept Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9798331531768;9798331531751

This paper presents a groundbreaking solution to minimize distribution network losses by optimizing D-STATCOM ( Distribution Static Compensator) device allocation. The proposed method uses Quantum Predator-Prey Brain Storm Optimization (QPPBSO), which integrates Predator-Prey and Quantum -computing superposition strategies to Brain Storm Optimization (BSO). The optimization problem is expressed as a mixed integer nonlinear programming (MINLP) problem, where the location and output variables of D-FACTS are optimized. With the rise of renewable energy, uncertainties in active distribution networks need to be considered in optimization problems. To address this issue, the proposed method employs Robust Optimization (RO), which ensures that the optimal allocation of D-STATCOM devices is robust to uncertainties. The simulation results indicate the effectiveness of the proposed method through testing it on the 69 -node distribution system.

关键词： D-FACTS loss minimization distribution networks evolutionary computation Predator-Prey Brain Storm Optimization robust optimization quantum computing

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Personalized Privacy-Preserving Semi-Centralized Recommendation System in a Trust-Based Agent Network 22

Personalized Privacy-Preserving Semi-Centralized Recommendat...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wen, Qi Leung, Carson K. Pazdor, Adam G. M. Univ Manitoba Winnipeg MB Canada

ISBN: (纸本)9798350381993;9798350382006

In the current big data era, recommendation systems play an important role in our daily life to help us make faster and better decisions from massive numbers of choices. Personalized recommendation has gained its popularity as it provides recommendations according to the user profile, preferences and/or interests. Many existing systems make recommendation by centralizing data. However, the exposure of sensitive data raises a privacy concern as research has shown that it is possible to de-identify anonymous users. Examples include inferring sensitive information (e.g., political views, sexual orientations) from nonsensitive data (e.g., movie ratings). In this paper, we present a personalized privacy-preserving recommendation system called Trust-based Agent Network (TAN). It tackles the privacy issue by semi-decentralizing data and treating each node in the network as an agent. As such, data are distributed to each agent within each trusted network, and the recommendation service provider collects only obfuscated data from agents by adopting the differential-privacy mechanism. Consequently, data in our TAN are either protected inside local trusted networks or obfuscated outside of trusted networks. Final recommendation can then be made by aggregating the local suggestions from the trusted network and obfuscated global suggestions from the service provider. Personalized recommendations can be made by putting more emphasize on local suggestions. Evaluation results show that our TAN leads to high accuracy and highly personalized recommendations while protecting privacy.

关键词： Recommendation system Personalization Collaborative filtering Semi-centralized Trusted network Privacy preservation Ubiquitous computing

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Towards Efficient and Reconfigurable Neuromorphic computing using an Embedded FPGA in 22nmFDX 22

Towards Efficient and Reconfigurable Neuromorphic Computing ...

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22nd IEEE Interregional NEWCAS conference (NEWCAS)

作者： Ghonem, Ahmed Zaky Elewa, Sherif Maher Tawfik, Eslam Yahya Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9798350361766;9798350361759

There is a rising demand for AI workloads running on the edge that are increasingly complex, calling for more efficient computing platforms. Neuromorphic computing is a class of electronic circuits that aim to mimic a biological brain's functionality and computational efficiency. Its applications scale from low-duty cycles high-performance tasks to high-duty cycle ultra-low power operations. Embedded FPGAs (eFPGAs) can address these demands given their flexibility compared to ASIC, low latency, and lower energy consumption compared to CPUs. eFPGAs provide adaptive performance and enable prototyping and deployment of new designs post-silicon manufacturing. In this work, we aim to combine the reconfigurability of the eFPGA with the efficiency of digital neuromorphic circuits to provide efficient and flexible hardware solutions for AI workloads. We evaluate primitives found in a typical neuromorphic computing system, such as rate coding, Izhikevich (IZH) neuron model, and spike-time-dependent-plasticity (STDP) learning rule on an eFPGA fabricated in 22nmFDX. We report the power and utilization of these primitives and propose architectural improvements to the eFPGA to better implement a complete and reconfigurable neuromorphic processor for various AI workloads.

关键词： Embedded FPGA Izhikevich Neuron model Leaky-Integrate-and-Fire Online-Learning Rate Encoding Reconfigurable computing Spiking Neural Networks STDP

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Two-level Computation Offloading to Optimize the Energy Consumption of UAV-mounted Edge Nodes 10

Two-level Computation Offloading to Optimize the Energy Cons...

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10th Workshop on Micro Aerial Vehicle Networks, systems, and Applications (DroNet) / 22nd Annual international conference on Mobile systems, Applications and Services (MOBISYS)

作者： Srivastava, Parth Peddoju, Sateesh K. Indian Inst Technol Roorkee Roorkee Uttarakhand India

ISBN: (纸本)9798400706561

The utilization of Unmanned Aerial Vehicles (UAVs) in Internet of Things (IoT) applications has recently gained widespread popularity. Recently, fixed-edge servers have been used for computation offloading services to IoT devices. These nodes have troublesome installations in geographically constrained areas and are not easy to replace in case of sudden failure in remote areas. UAV nodes can be utilized as mobile Edge servers to overcome these problems. Their importance can be visualized in various applications like disaster management, offshore industries, military applications, healthcare, and wildlife conservation. Since UAVs run on battery, saving energy becomes a primary challenge. This paper proposes an architecture that provides a two-level offloading mechanism for IoT devices to minimize energy consumption. When computation offloading happens from IoT devices to their respective UAV-mounted Edge nodes, they do not need to wait if Edge server capacity is exhausted. In such a case, UAV offloads to another suitable one in its network. In this way, Edge nodes are more efficiently utilized than when a single UAV-mounted Edge node has to serve all its IoT devices. Using our model, we can also provide better services to IoT users. The proposed model uses game theory to utilize the Nash Equilibrium to solve this problem. The simulation work evaluates the accuracy of the proposed architecture and finds a significant difference in energy consumption between the case of offloading from IoT devices to respective UAVs and the case of the proposed model.

关键词： Unmanned Aerial Vehicles Edge computing Internet of Things Computation offloading

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Design of a Reconfigurable Activation Function for All-Optical Neural Networks 22

Design of a Reconfigurable Activation Function for All-Optic...

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22nd IEEE Interregional NEWCAS conference (NEWCAS)

作者： Destras, Oceane Le Beux, Sebastien de Magalhaes, Felipe Gohring Nicolescu, Gabriela Polytech Montreal Dept Genie Informat & Genie Logiciel Montreal PQ Canada Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9798350361766;9798350361759

Photonic integrated circuits present a promising avenue for the integration of Deep Neural Networks (DNNs), offering solutions to the speed and power consumption constraints inherent to their electronic counterparts. Notably, research showcasing the ability of photonic integrated circuits to realize matrix multiplications - a crucial operation in DNNs - at the speed of light has drawn much attention to the field of optical neural networks (ONNs). One of the challenges of designing fully optical DNNs is the photonic integration of the activation function, a nonlinear function. Optical nonlinear responses often deviate in shape from traditional DNN activation functions. A pivotal requirement for standardizing ONN architectures without sacrificing flexibility is the development of a fully tunable optical activation function. Presently, reconfigurable optical activation functions exhibit limitations in reproducing diverse functions, constraining the potential of photonic DNNs. In this article, we propose an architecture leveraging Mach-Zehnder interferometers and saturable absorbers to execute a range of activation functions, including ReLU, sigmoid, and tanh.

关键词： Photonic integrated circuits Optical computing Nonlinear optics Neural networks

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A Framework for Privacy Policy Enforcement for Connected Automotive systems 22

A Framework for Privacy Policy Enforcement for Connected Aut...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Bkakria, Anis Brika, Lydia IRT SystemX Palaiseau France

ISBN: (纸本)9798350381993;9798350382006

The GDPR is a set of regulations designed to give users control over their personal data and applies to any connected object that processes such data. One of the most complex environments covered by the GDPR is the connected car, which has brought attention to the sensitive data processed by these vehicles. This data includes information about the driver or owner, the vehicle's environment, and the vehicle itself. Given the varying sensitivity levels of this data, it is important to offer users control over their data and privacy. To address this, we propose an end-to-end privacy preserving framework that ensures the integrity, confidentiality, and traceability of data related to user privacy within a connected vehicle. This framework combines data tainting for data traceability, lightweight signature for data integrity, and attribute-based encryption for confidentiality and access control to effectively enforce privacy policies set by both vehicle users and manufacturers. This approach is the first to offer end-to-end privacy and confidentiality policy enforcement for connected vehicles, covering data generation by sensors to the Transmission Control Unit.

关键词： Privacy enhancing technologies Attribute-based encryption Lightweight signature Data tainting

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R-SACE: RIS-Enabled Sensing-Aided Communication Enhancement in ISAC systems 22

R-SACE: RIS-Enabled Sensing-Aided Communication Enhancement ...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Li, Xiaohui Chen, Yunpei Wang, Hong Sheng, Shuran Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing Peoples R China Taiyuan Univ Technol Coll Elect Informat & Opt Engn Taiyuan Peoples R China Shanghai Dianji Univ Sch Elect Informat Engn Shanghai Peoples R China

ISBN: (纸本)9798350381993;9798350382006

Integrated sensing and communication (ISAC) and reconfigurable intelligent surface (RIS) are two critical enablers for sixth-generation (6G) communication systems. Sensing-aided communication enhancement (SACE) is one of the foreseeable scenarios of ISAC. This paper focuses on a RIS-assisted SACE (R-SACE) mechanism where the base station (BS) acts as a sensor and communicator. In particular, the RIS and the sensing at the BS assist in suppressing harmful direct interference and meanwhile reconstructing another feasible communication link. The average achievable capacity maximization problem of the RIS-assisted communication link is investigated by jointly optimizing the time resource allocation within a coherent ISAC time frame and the reflecting recoding of the RIS under the constraints of sensing performance and interference. Alternating algorithm (AO) and particle swarm optimization (PSO) methods are utilized to solve the above optimization problem. Simulation results validate the performance gains of the proposed R-SACE mechanism in suppressing interference and enhancing average achievable capacity.

关键词： Integrated sensing and communication reconfigurable intelligent surface sensing-aided communication enhancement capacity maximization

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