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Implementation of Data Mining on a secure cloud computing over a Web API using Supervised Machine Learning Algorithm Data Mining in a secure cloud computing Environment through Restful API

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第5期13卷 1-4页

作者： Ige, Tosin Sikiru, Adewale Univ Texas El Paso Dept Comp Sci El Paso TX 79968 USA Virginia Technol Univ Dept Comp Sci Blacksburg VA USA

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model.

关键词： cloud computing data warehouse data mining window service Web API machine learning algorithm secure cloud computing

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Blockchain as a Platform for secure cloud computing Services 22

Blockchain as a Platform for Secure Cloud Computing Services

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22nd IEEE International Conference on Advanced Communication Technology (ICACT)

作者： Tsai, Wang-You Chou, Tzu-Chuan Chen, Jiann-Liang Ma, Yi-Wei Huang, Chen-Jui Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei Taiwan

ISBN: (纸本)9791188428045

Problems related to privacy and cyber-attacks have increased in recent years as a result of the rapid development of cloud computing. This work concerns secure cloud computing services on a blockchain platform, called cloud@blockchain, which benefit from the anonymity and immutability of blockchain. Two functions- anonymous file sharing and inspections to find illegally uploaded files- on cloud@blockchain are designed. On cloud@blockchain, cloud users can access data through smart contracts, and recognize all users within the application layer. The performance of three architectures- a pure blockchain, a hybrid blockchain with cache and a traditional database in accessing data is analyzed. The results reveal the superiority of the hybrid blockchain with the cache over the pure blockchain and the traditional database, which it outperforms by 500% and 53.19% respectively.

关键词： Blockchain as a Platform secure cloud computing Cyber-Attack Privacy Smart Contract

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secure cloud computing: Continuous Anomaly Detection Approach in Legal Metrology

Secure Cloud Computing: Continuous Anomaly Detection Approac...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Discovering New Horizons in Instrumentation and Measurement

作者： Oppermann, Alexander Toro, Federico Grasso Thiel, Florian Seifert, Jean-Pierre Physikal Tech Bundesanstalt PTB Dept Metrol IT 85 Berlin Germany Tech Univ Berlin Secur Telecommunicat Berlin Germany

ISBN: (纸本)9781538622223

In the field of Legal Metrology, it shall be assured that all measurements are carried out by a trusted computer system. Securing Computer Systems, especially in a distributed environment can be a challenging task to comply, since, in such an environment, a measurement instrument cannot be simply disconnected to guarantee its security. Following the approach of continuous monitoring to derive a normal system behavior can ensure continuous anomaly detection to satisfy the needed demands of Legal Metrology. The proposed Anomaly Detection approach measures the severity of the detected incident and places it into three different categories: green, yellow and red. The presented module can detect attacks against distributed applications in a cloud computing environment, using pattern recognition techniques for clustering, as well as statistical approaches. Both, inexperienced and experienced attacks have been tested and results are presented.

关键词： Anomaly Detection Continuous Monitoring secure cloud computing Anomaly Detection as a Service Distributed computing Legal Metrology

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On the Security of Homomorphic Encryption Schemes with Restricted Decryption Oracles

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Journal of Systems Science & Complexity 2024年第5期37卷 2240-2261页

作者： MA Guangsheng LI Hongbo School of Mathematics and Physics North China Electric Power UniversityBeijing 100096China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Fully homomorphic encryption(FHE)can be used for privacy-preserving aggregation of medical *** this typical application,the security against passive attacks has been well studied by Li and Micciancio(2021).In this paper,the authors further consider a“nearly passive”kind of attack,where the attacker may behave like a passive attacker in the view of the third-party *** capture the security against this hard-to-detect attack,the authors propose a new notion of IND-CPA^(rD)*** authors show that the standard LWE encryption and its related FHE schemes can not defend against IND-CPA^(rD)attack,even under a stricter rule limiting the content and number of queries made by the *** make the application of FHE schemes more secure,the authors discuss some possible modifications that may serve as countermeasures to IND-CPA^(rD)attack.

关键词： Homomorphic encryption privacy secure cloud computing security

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Juliet: A Configurable Processor for computing on Encrypted Data

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IEEE TRANSACTIONS ON COMPUTERS 2024年第9期73卷 2335-2349页

作者： Gouert, Charles Mouris, Dimitris Tsoutsos, Nektarios Georgios Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA

Fully homomorphic encryption (FHE) has become progressively more viable in the years since its original inception in 2009. At the same time, leveraging state-of-the-art schemes in an efficient way for general computation remains prohibitively difficult for the average programmer. In this work, we introduce a new design for a fully homomorphic processor, dubbed Juliet, to enable faster operations on encrypted data using the state-of-the-art TFHE and cuFHE libraries for both CPU and GPU evaluation. To improve usability, we define an expressive assembly language and instruction set architecture (ISA) judiciously designed for end-to-end encrypted computation. We demonstrate Juliet's capabilities with a broad range of realistic benchmarks including cryptographic algorithms, such as the lightweight ciphers Simon and Speck, as well as logistic regression (LR) inference and matrix multiplication.

关键词： Cryptography Noise Circuits Libraries cloud computing Computers Assembly Applied cryptography encrypted processor hardware acceleration homomorphic encryption privacy-enhancing technologies privacy-preserving ISA private computation secure cloud computing

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Efficient FHE-Based Privacy-Enhanced Neural Network for Trustworthy AI-as-a-Service

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IEEE TRANSACTIONS ON DEPENDABLE AND secure computing 2024年第5期21卷 4451-4468页

作者： Lam, Kwok-Yan Lu, Xianhui Zhang, Linru Wang, Xiangning Wang, Huaxiong Goh, Si Qi Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Nanyang Technol Univ Singapore 639798 Singapore Nanyang Technol Univ Div Math Sci Singapore 639798 Singapore Chinese Acad Sci Inst Informat Engn Beijing 100045 Peoples R China

AI-as-a-Service has emerged as an important trend for supporting the growth of the digital economy. Digital service providers make use of their vast amount of customer data to train AI models (such as image recognition, financial modelling and pandemic modelling etc) and offer them as a service on the cloud. While there are convincing advantages for using such third-party models, the fact that model users are required to upload their data to the cloud is bound to raise serious privacy concerns, especially in the face of increasingly stringent privacy regulations and legislation. To promote the adoption of AI-as-a-Service while addressing privacy issues, we propose a practical approach for constructing privacy-enhanced neural networks by designing an efficient implementation of fully homomorphic encryption. With this approach, an existing neural network can be converted to process FHE-encrypted data and produce encrypted output which are only accessible by the model users, and more importantly, within an operationally acceptable time (e.g., within 1 s for facial recognition in typical border control systems). Experimental results show that in many practical tasks such as facial recognition, text classification and so on, we obtained the state-of-the-art inference accuracy in less than one second on a 16 cores CPU.

关键词： Neural networks Data models Artificial intelligence Table lookup Face recognition Biological system modeling Servers Fully homomorphic encryption privacy-enhanced neural networks look-up table algorithm deep neural network digital trust secure cloud computing data privacy cryptographic protocol applied cryptography

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secure Deep Learning Framework for Moving Object Detection in Compressed Video

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IEEE TRANSACTIONS ON DEPENDABLE AND secure computing 2024年第4期21卷 2836-2851页

作者： Tian, Xianhao Zheng, Peijia Huang, Jiwu Shenzhen Univ Coll Informat Engn Shenzhen 518052 Guangdong Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangdong Prov Key Lab Informat Secur Technol Guangzhou 510006 Guangdong Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen Key Lab Media Secur Shenzhen 518052 Guangdong Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Guangdong Peoples R China

In the cloud, there is an urgent need to implement intelligent video surveillance in a privacy-preserving way. Moving object detection is an important task in the intelligent surveillance system. In this article, we propose a privacy-preserving deep learning framework to detect moving objects on compressed videos. We encrypt video bitstreams using selective video encryption to protect the private video content. We propose encrypted domain motion information (EDMI) without decryption and decompression to design three motion feature maps. Due to the sparsity of the EDMI distribution, existing convolutional backbones designed for RGB images have difficulty providing satisfactory performance. We design a novel convolutional backbone using a "subtraction" strategy to reduce model complexity. Our backbone employs residual blocks and skipping connections to reuse the EDMI at deeper layers. We evaluate our model on two large high-definition surveillance video datasets, i.e., VIRAT and Duke-MTMC. The experimental results show that the proposed framework achieves state-of-the-art detection performance compared with the most recent works. Our approach achieves an excellent privacy-utility tradeoff. Compared to previous solutions, it performs more robustly in crowded scenarios with challenges like occlusion. To our best knowledge, this is the first reported deep learning framework for moving object detection in encrypted-compressed video.

关键词： Cryptography Encryption Streaming media cloud computing Surveillance Privacy Task analysis Deep learning encrypted domain moving object detection secure cloud computing

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secure computing Enclaves Using FPGAs

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IEEE TRANSACTIONS ON DEPENDABLE AND secure computing 2021年第2期18卷 593-604页

作者： Elrabaa, M. E. S. Al-Asli, M. Abu-Amara, M. King Fahd Univ Petr & Minerals Comp Engn Dept Dhahran 31261 Saudi Arabia

A new scheme for securing users' data and applications in public clouds and data centers using Field Programmable Gate Arrays (FPGAs) has been developed. This scheme incorporates all necessary protocols, hardware, and software components to provide protection against many known potential attacks including internal attacks. It achieves perfect forward secrecy, provides FPGA authentication and integrity checks, and securely establishes a symmetric session key between the user and the FPGA. A complete prototype has been implemented to show the feasibility of the proposed scheme with current FPGAs. Experimental results showed that an FPGA-based compute node can be set up in a cloud in 3.36s;12.6 times faster than booting a medium-size conventional Virtual Machine (VM) on the same cloud. Based on the average global Internet speed, the time it takes to set up the FPGA-based machine from anywhere in the world was estimated to be 15s. Also, running an experimental secure image processing application on the FPGA took 50 percent less time than running the same application on a conventional state-of-the art processor (without a secure container).

关键词： cloud computing Field programmable gate arrays Random access memory Software Cryptography Containers Cryptographic protocols and algorithms key management hardware security secure cloud computing FPGAs

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Multi-User Dynamic Searchable Symmetric Encryption With Corrupted Participants

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IEEE TRANSACTIONS ON DEPENDABLE AND secure computing 2023年第1期20卷 114-130页

作者： Chamani, Javad Ghareh Wang, Yun Papadopoulos, Dimitrios Zhang, Mingyang Jalili, Rasool Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Sharif Univ Technol Dept Comp Engn Tehran Iran Huawei Technol Poisson Lab Beijing 518129 Peoples R China

We study the problem of multi-user dynamic searchable symmetric encryption (DMUSSE) where a data owner stores its encrypted documents on an untrusted remote server and wishes to selectively allow multiple users to access them by issuing keyword search queries. Specifically, we consider the case where some of the users may be corrupted and colluding with the server to extract additional information about the dataset (beyond what they have access to). We provide the first formal security definition for the dynamic setting as well as forward and backward privacy definitions. We then propose mu SE, the first provably secure DMUSSE scheme and instantiate it in two versions, one based on oblivious data structures and one based on update queues, with different performance trade-offs. Furthermore, we extend mu SE to support verifiability of results. To achieve this, users need a secure digest initially computed by the data owner and changed after every update. We efficiently accommodate this, without relying on a trusted third party, by adopting a blockchain-based approach for the digests' dissemination and deploy our schemes over the permissioned Hyperledger Fabric blockchain. We prototype both versions and experimentally evaluate their practical performance, both as stand-alone systems and running on top of Hyperledger Fabric.

关键词： Servers Encryption Cryptography Fabrics Blockchains Privacy Outsourcing Dynamic multi-user searchable symmetric encryption secure cloud computing data outsourcing

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Efficient and secure MPC Through Integration of FHE and Proxy Re-encryption

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SN Computer Science 2025年第2期6卷 1-10页

作者： Upadhyay, Rohitkumar R. Padhye, Sahadeo Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj 211004 India

secure multi-party computation (MPC) protocols play a crucial role in facilitating collaboration among distrustful parties while safeguarding individual privacy. Traditional MPC protocols rely on private communication channels and secret sharing schemes, which may not be feasible in scenarios with limited computational resources or intermittent connectivity. Fully Homomorphic Encryption (FHE) offers a promising solution, but its practical implementation for multi-party scenarios poses challenges such as huge storage complexity, interactive key generation, and decryption. In this work, we introduce the FHE-Pro scheme, which combines FHE with Proxy Re-encryption to address these challenges. Proxy re-encryption allows for secure delegation of decryption rights, enabling non-interactive key generation and decryption processes in MPC protocols. Our scheme offers a practical and efficient solution for secure multi-party computations, considering the specific requirements and constraints of diverse scenarios. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fully homomorphic encryption Multi-party computation Privacy-preserving computation Proxy re-encryption secure cloud computing

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