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A secure data analytics scheme for multimedia communication in a decentralized smart grid

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第24期81卷 34797-34822页

作者： Kumari, Aparna Tanwar, Sudeep Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad Gujarat India

With the exponential increase in energy demands (commercial as well as residential), the traditional grid infrastructure significantly shifted to intelligent ICT-based Smart Grid (SG) infrastructure. In the SG environment, only efficient energy management may not be sufficient as the SG dynamics have significant impacts on multimedia communications such as video surveillance of the technical/non-technical losses of energy and many more. The inevitable energy losses can be identified by process and analyze the massive amount of heterogeneous data, i.e., Big data (BD) generated through smart devices such as sensors, Smart Meters (SMs), and others. The key challenges in analyzing multimedia BD are computational complexity, operational integration complexity, data security, and privacy. To overcome the aforementioned issues, this paper proposes a blockchain-based data analytics scheme called ChoIce, which offers secure data collection, analysis, and decision support for the SG systems. It works in two phases;(i) secure data collection over Ethereum and (ii) BD analytics and decision-making using deep learning (DL). The robust and secure data analytics, efficient network management, and high-performance computing for BD are crucial towards the optimization of SG operation. The performance of ChoIce is evaluated considering parameters such as the data storage cost, multimedia communication latency, and prediction accuracy. Thus, the results of ChoIce shows that it outperforms in contrast to other state-of-the-art approaches.

关键词： Multimedia system Big data Smart grid secure data analytics Blockchain Deep learning data security & privacy

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A Programming API Implementation for secure data analytics Applications with Homomorphic Encryption on GPUs 28

A Programming API Implementation for Secure Data Analytics A...

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28th Annual IEEE International Conference on High Performance Computing, data, and analytics (HiPC)

作者： Lou, Shuangsheng Agrawal, Gagan Ohio State Univ Comp Sci & Engn Columbus OH 43210 USA Augusta Univ Comp & Cyber Sci Augusta GA USA

ISBN: (纸本)9781665410168

As sensitive data is frequently stored and processed in environments that are either shared, untrusted or otherwise can be compromised, privacy is frequently a concern. To address this, a method that has been gaining popularity is to use Homomorphic Encryption (HE), which allows computation over encrypted data, i.e., without decrypting the data first. However, the overhead of such analytics (up to 4 orders of magnitude) is a detriment and while there have been a few previous efforts on reducing these overheads through the use of accelerators like GPUs, programmability is a concern. This paper addresses both performance and programmability concerns with the use of HE. We port the major pieces of Simple Encrypted Arithmetic Library (SEAL) from Microsoft to GPU using CUDA. Through these GPU-based functions, a new HE application can be developed easily. We demonstrate this by developing encrypted versions of three applications - CNN, k-means, and KNN. The speedups of execution time for CNN, k-means and KNN on a single GPU over CPU implementation achieve up to 81, 133 and 7 respectively.

关键词： Homomorphic Encryption secure data analytics Cloud applications Security Programmability GPU

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Machine Learning Models for secure data analytics: A taxonomy and threat model

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COMPUTER COMMUNICATIONS 2020年第0期153卷 406-440页

作者： Gupta, Rajesh Tanwar, Sudeep Tyagi, Sudhanshu Kumar, Neeraj Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad Gujarat India Thapar Inst Engn & Technol Deemed Be Univ Dept Elect & Commun Engn Patiala Punjab India Thapar Inst Engn & Technol Deemed Be Univ Dept Comp Sci Engn Patiala Punjab India Asia Univ Dept Comp Sci & Informat Engn Taichung Taiwan King Abdulaziz Univ Jeddah Saudi Arabia

In recent years, rapid technological advancements in smart devices and their usage in a wide range of applications exponentially increases the data generated from these devices. So, the traditional data analytics techniques may not be able to handle this extreme volume of data known as Big data (BD) generated by different devices. However, this exponential increase of data opens the doors for the different type of attackers to launch various attacks by exploiting various vulnerabilities (SQL injection, OS fingerprinting, malicious code execution, etc.) during data analytics. Motivated from the aforementioned discussion, in this paper, we explored Machine Learning (ML) and Deep Learning (DL)-based models and techniques which are capable off to identify and mitigate both the known as well as unknown attacks. ML and DL-based techniques have the capabilities to learn from the traffic pattern using training and testing datasets in the extensive network domains to make intelligent decisions concerning attack identification and mitigation. We also proposed a DL and ML-based secure data analytics (SDA) architecture to classify normal or attack input data. A detailed taxonomy of SDA is abstracted into a threat model. This threat model addresses various research challenges in SDA using multiple parameters such as-efficiency, latency, accuracy, reliability, and attacks launched by the attackers. Finally, a comparison of existing SDA proposals with respect to various parameters is presented, which allows the end users to select one of the SDA proposals in comparison to its merits over the others.

关键词： Big data secure data analytics data reduction Machine learning models Threat model data security and privacy

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secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2020年 28卷

作者： Kumari, Aparna Tanwar, Sudeep Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 Gujarat India

A smart city requires an intelligent infrastructure to improve the quality of life with sustainable environment for its citizens. There is an exponential demand for efficient, secure, reliable, and uninterrupted electricity supply, so there is a need for an intelligent grid, which uses Information and Communications Technology (ICT) to optimize the generation, circulation, and ingestion of electricity. Thus, Smart Grid (SG) acts as an intelligent grid, which plays an important role in the overall growth of any smart city. Further, the Big data (BD) generated from SG, provides noteworthy information that could significantly benefit different applications of SG, such as demand response and load profiling. However, an insecure technique for decision-making may lead to the breach of SG data where hackers gained full access to consumer data. On the contrary, a secure technique for decision-making can provide satisfaction to all the stakeholders, including consumers and utility providers. Motivated from these facts, this paper presents a comprehensive literature survey and analysis of state-of-the-art proposals for secure data analytics (SDA) in the SG system. However, to achieve SDA for the SG systems is one of the critical tasks. The existing research and development endeavors not fully exploited the SDA in the SG system. In this paper, we discuss the distinctive nature of SDA and its complexity over the SG data. A detailed taxonomy abstracted into a novel process model, which highlights various research challenges such as secure data collection and preprocessing, secure load data processing and storage, load prediction, load management and analysis, data security and privacy issues, and data communication. Finally, a case study is presented to demonstrate the process model.

关键词： Big data secure data analytics Sustainable smart city Load forecasting Smart meter data security and privacy Machine learning

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secure Artificial Intelligence of Things for Implicit Group Recommendations

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IEEE INTERNET OF THINGS JOURNAL 2022年第4期9卷 2698-2707页

作者： Yu, Keping Guo, Zhiwei Shen, Yu Wang, Wei Lin, Jerry Chun-Wei Sato, Takuro Waseda Univ Global Informat & Telecommun Inst Tokyo 1698555 Japan Chongqing Technol & Business Univ Sch Artificial Intelligence Chongqing 400067 Peoples R China Chongqing Technol & Business Univ Natl Res Base Intelligent Mfg Serv Chongqing 400067 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci N-5063 Bergen Norway Waseda Univ Res Inst Sci & Engn Tokyo 1698555 Japan

The emergence of Artificial Intelligence of Things (AIoT) has provided novel insights for many social computing applications, such as group recommender systems. As the distances between people have been greatly shortened, there has been more general demand for the provision of personalized services aimed at groups instead of individuals. The existing methods for capturing group-level preference features from individuals have mostly been established via aggregation and face two challenges: 1) secure data management workflows are absent and 2) implicit preference feedback is ignored. To tackle these current difficulties, this article proposes secure AIoT for implicit group recommendations (SAIoT-GRs). For the hardware module, a secure Internet of Things structure is developed as the bottom support platform. For the software module, a collaborative Bayesian network model and noncooperative game are introduced as algorithms. This secure AIoT architecture is able to maximize the advantages of the two modules. In addition, a large number of experiments are carried out to evaluate the performance of SAIoT-GR in terms of efficiency and robustness.

关键词： Bayesian network group recommender systems (GRSs) noncooperative game secure data analytics

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Homomorphic Encryption for secure data Analysis: A Hybrid Approach using PKCS1_OAEP Padding 2

Homomorphic Encryption for Secure Data Analysis: A Hybrid Ap...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Shanmukha Aditya, G. Kruthika, B. Rajagopal, Shinu M. Kavitha, C.R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

In today's data-driven world, striking a balance between data utility and privacy is paramount. Homomorphic encryption, a revolutionary cryptographic technique, facilitates computations on encrypted data without decryption. This study addresses the growing demand for secure data analysis, emphasizing the seamless integration of homomorphic encryption in connecting robust data analysis with essential privacy safeguards. Serving as a vital component in this context, this cutting-edge technique proves pivotal in fostering a secure, privacy-centric approach to data analytics. The outcomes underscore its significance, representing a substantial stride towards the evolution of a conscientious and responsible data-driven ecosystem. The achieved results signal progress in harmonizing data utility and privacy, enhancing overall security and ethical data practices without compromising analytical capabilities. © 2024 IEEE.

关键词： AES Encryption Cloud Security Homomorphic Encryption Privacy Preservation RSA Encryption secure data analytics

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Vizard: A Metadata-hiding data Analytic System with End-to-End Policy Controls 22

Vizard: A Metadata-hiding Data Analytic System with End-to-E...

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28th ACM SIGSAC Conference on Computer and Communications Security (CCS)

作者： Cai, Chengjun Zang, Yichen Wang, Cong Jia, Xiaohua Wang, Qian City Univ Hong Kong Dongguan Res Inst Hong Kong Peoples R China City Univ Hong Kong Hong Kong Peoples R China Wuhan Univ Wuhan Peoples R China

ISBN: (纸本)9781450394505

Owner-centric control is a widely adopted method for easing owners' concerns over data abuses and motivating them to share their data out to gain collective knowledge. However, while many control enforcement techniques have been proposed, privacy threats due to the metadata leakage therein are largely neglected in existing works. Unfortunately, a sophisticated attacker can infer very sensitive information based on either owners' data control policies or their analytic task participation histories (e.g., participating in a mental illness or cancer study can reveal their health conditions). To address this problem, we introduce Vizard, a metadata-hiding analytic system that enables privacy-hardened and enforceable control for owners. Vizard is built with a tailored suite of lightweight cryptographic tools and designs that help us efficiently handle analytic queries over encrypted data streams coming in real-time (like heart rates). We propose extension designs to further enable advanced owner-centric controls (with AND, OR, NOT operators) and provide owners with release control to additionally regulate how the result should be protected before deliveries. We develop a prototype of Vizard that is interfaced with Apache Kafka, and the evaluation results demonstrate the practicality of Vizard for large-scale and metadata-hiding analytics over data streams.

关键词： secure data analytics End-to-End control Metadata privacy

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SGX-BigMatrix: A Practical Encrypted data Analytic Framework With Trusted Processors 17

SGX-BigMatrix: A Practical Encrypted Data Analytic Framework...

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24th ACM-SIGSAC Conference on Computer and Communications Security (ACM CCS)

作者： Shaon, Fahad Kantarcioglu, Murat Lin, Zhiqiang Khan, Latifur Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9781450349468

Recently, using secure processors for trusted computing in cloud has attracted a lot of attention. Over the past few years, efficient and secure data analytic tools (e.g., map-reduce framework, machine learning models, and SQL querying) that can be executed over encrypted data using the trusted hardware have been developed. However, these prior efforts do not provide a simple, secure and high level language based framework that is suitable for enabling generic data analytics for non-security experts who do not have concepts such as "oblivious execution". In this paper, we thus provide such a framework that allows data scientists to perform the data analytic tasks with secure processors using a Python/Matlablike high level language. Our framework automatically compiles programs written in our language to optimal execution code by managing issues such as optimal data block sizes for I/O, vectorized computations to simplify much of the data processing, and optimal ordering of operations for certain tasks. Furthermore, many language constructs such as if-statements are removed so that a non-expert user is less likely to create a piece of code that may reveal sensitive information while allowing oblivious data processing (i.e., hiding access patterns). Using these design choices, we provide guarantees for efficient and secure data analytics. We show that our framework can be used to run the existing big data benchmark queries over encrypted data using the Intel SGX efficiently. Our empirical results indicate that our proposed framework is orders of magnitude faster than the general oblivious execution alternatives.

关键词： secure data analytics Intel SGX

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