Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems

Choi, Wooseok; Tomei, Matthew; Vicarte, Jose Rodrigo Sanchez; Hanumolu, Pavan Kumar; Kumar, Rakesh

doi:10.1109/isca.2018.00053

Cited by 33 publications

(14 citation statements)

References 32 publications

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“…In recent years, there has been a considerable research interest in privacy-preserving machine learning [21][22][23][24][25][26][27]. This technique combines machine learning techniques (such as logistic regression, SVM, artificial neural networks, etc.)…”

Section: Privacy-preserving Machine Learningmentioning

confidence: 99%

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Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

Senekane

2019

MAKE

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The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.

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Section: Privacy-preserving Machine Learningmentioning

confidence: 99%

“…Choi et al [27] propose a privacy-preserving scheme for application in Internet of Things (IoT) systems which use ultra-low power. This proposed scheme uses local Differential Privacy.…”

Section: Privacy-preserving Machine Learningmentioning

confidence: 99%

Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

Senekane

2019

MAKE

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“…There are many potential attacks, such as NILM, that can reveal the private data before they reach the trusted data curator. 20,21 Furthermore, electric power grid users might be sensitive military industrial enterprises, and electric power provider cannot be regarded as trusted third party, moreover the widely uncontrollable channels. 22 In summary, in Power IoTs, the support of trusted data curator is inadequate, therefore local obfuscation is a better choice for users’ behavior privacy preserving in Power IoTs.…”

Section: Introductionmentioning

confidence: 99%

“…Many researches had indicated that naive hardware implementation of LDP mechanism cannot guarantee behavior privacy of individual user in Power IoT systems. 20 Some advanced obfuscation mechanisms 24–28 combined with distribution estimation or machine learning are able to achieve the trade-off; however, the following problem is that the naive Power IoT device cannot endure the complex and high computational cost algorithms, especially the procedure of model training that high-computing resource consumption. In summary, Power IoTs with naive hardware cannot guarantee behavior privacy completely through directly applying existing local obfuscation mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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IFed: A novel federated learning framework for local differential privacy in Power Internet of Things

Cao

Liu

Zhao

et al. 2020

International Journal of Distributed Sensor Networks

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Nowadays, wireless sensor network technology is being increasingly popular which is applied to a wide range of Internet of Things. Especially, Power Internet of Things is an important and rapidly growing section in Internet of Thing systems, which benefited from the application of wireless sensor networks to achieve fine-grained information collection. Meanwhile, the privacy risk is gradually exposed, which is the widespread concern for electricity power consumers. Non-intrusive load monitoring, in particular, is a technique to recover state of appliances from only the energy consumption data, which enables adversary inferring the behavior privacy of residents. There can be no doubt that applying local differential privacy to achieve privacy preserving in the local setting is more trustworthy than centralized approach for electricity customers. Although it is hard to control the risk and achieve the trade-off between privacy and utility by traditional local differential privacy obfuscation mechanisms, some existing obfuscation mechanisms based on artificial intelligence, called advanced obfuscation mechanisms, can achieve it. However, the large computing resource consumption to train the machine learning model is not affordable for most Power Internet of Thing terminal. In this article, to solve this problem, IFed was proposed—a novel federated learning framework that let electric provider who normally is adequate in computing resources to help Power Internet of Thing users. First, the optimized framework was proposed in which the trade-off between local differential privacy, data utility, and resource consumption was incorporated. Concurrently, the following problem of privacy preserving on the machine learning model transport between electricity provider and customers was noted and resolved. Last, users were categorized based on different levels of privacy requirements, and stronger privacy guarantee was provided for sensitive users. The formal local differential privacy analysis and the experiments demonstrated that IFed can fulfill the privacy requirements for Power Internet of Thing users.

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Privacy preserving and secure robust federated learning: A survey

Han,

Lu,

Wang

et al. 2024

Concurrency and Computation

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SummaryFederated learning (FL) has emerged as a promising solution to address the challenges posed by data silos and the need for global data fusion. It offers a distributed machine learning framework with privacy‐preserving features, allowing model training without the need to collect user data. However, FL also presents significant security and privacy threats that hinder its widespread adoption. The requirements of privacy and security in FL are inherently conflicting. Privacy necessitates the concealment of individual client updates, while security requires the disclosure of client updates to detect anomalies. While most existing research focused on the privacy and security aspects of FL, very few studies have addressed the compatibility of these two demands. In this work, we aim to bridge this gap by proposing a comprehensive defense scheme that ensures privacy, security, and compatibility in FL. We categorize the existing literature into two key directions: privacy defense and security defense. Privacy defense includes methods based on additive masks, differential privacy, homomorphic encryption, and trusted execution environment, whereas security defense encompasses distance‐, performance‐, clustering‐, and similarity‐based anomaly detection techniques and statistical information‐based anomaly update bypassing techniques when the server is trusted and privacy‐compatible anomaly update detection techniques when the server is not trusted. In addition, this article presents decentralized FL solutions based on blockchain. For each direction, we discuss specific technical solutions, their advantages, and disadvantages. By evaluating various defense methods, we identify the most suitable approach to address the primary challenge of “achieving a secure and robust FL system against malicious adversaries while protecting users' privacy.” We then propose a theoretical reference framework for end‐to‐end protection of privacy and security in FL for the key problem, which summarizes the attack surface of FL systems from the client to the server under the security model where the client and server are malicious. Leveraging the strengths and characteristics of existing schemes, our proposed framework integrates multiple techniques to strike a balance between privacy, usability, and efficiency. This framework serves as a valuable reference and provides insights for future work in the field. Finally, we also provide recommendations for future research directions in this field.

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Guaranteeing Local Differential Privacy on Ultra-Low-Power Systems

Cited by 33 publications

References 32 publications

Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

Differentially Private Image Classification Using Support Vector Machine and Differential Privacy

IFed: A novel federated learning framework for local differential privacy in Power Internet of Things

Privacy preserving and secure robust federated learning: A survey

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