SEPSI: A Secure and Efficient Privacy-Preserving Set Intersection with Identity Authentication in IoT

Liu, Bai; Zhang, Xiangyi; Shi, Runhua; Zhang, Mingwu; Zhang, Guoxing

doi:10.3390/math10122120

Cited by 6 publications

(4 citation statements)

References 43 publications

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“…To address these challenges, the literature suggests the need for innovative solutions that not only mitigate current concerns but are also adaptable to future technological advancements and threats. For instance, Bai Liu et al propose a quantum privacy-preserving set intersection protocol for IoT, exemplifying the kind of forward-thinking required to secure IoT networks against evolving cyber threats [63].…”

Section: Challengementioning

confidence: 99%

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2024

Preprint

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The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) has propelled technological innovation across various industries. This systematic literature review explores the current state and future trajectories of AI in IoT, with a particular focus on emerging trends in intelligent data analysis and privacy protection. The proliferation of IoT devices, marked by voluminous data generation, has reshaped data processing methods, providing actionable insights for informed decision-making. While previous reviews have offered valuable insights, they often fall short of comprehensively addressing the multifaceted dimensions within the AI-driven IoT landscape. This review aims to bridge this gap by systematically examining existing literature and acknowledging the limitations of past studies. To achieve this aim, the study uses a meticulous approach guided by established methodologies. The chosen methodology ensures the rigor and validity of the review, aligning with PRISMA guidelines for systematic reviews. This systematic literature review serves as a comprehensive guide for researchers, practitioners, and policymakers, offering insights into the current landscape and paving the way for future research directions. The identified trends and challenges provide a valuable resource for navigating the evolving domain of AI in IoT, fostering a balanced, secure, and sustainable advancement in this dynamic field.

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Section: Challengementioning

confidence: 99%

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2024

Preprint

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Section: Future Prospect and Challengesmentioning

confidence: 99%

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

Marengo

2023

Preprint

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This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the complexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regulations like GDPR. The research underscores the need for adaptable solutions that balance technological advancements with privacy and security considerations, setting a path for future exploration in AI-empowered IoT applications.

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“…A large and rapidly developing area of modern informatics deals with security and privacy of data (see, for example, [1][2][3][4][5][6]). In particular, the preservation of privacy is crucial for broad adoption of digital payments [7], healthcare applications [8], location-based services [9], telemedicine [10], monitoring industrial infrastructure [11] and the Internet of things [12,13].…”

Section: Introductionmentioning

confidence: 99%

Protecting Private Information for Two Classes of Aggregated Database Queries

Yang

Kelarev

et al. 2022

Informatics

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An important direction of informatics is devoted to the protection of privacy of confidential information while providing answers to aggregated queries that can be used for analysis of data. Protecting privacy is especially important when aggregated queries are used to combine personal information stored in several databases that belong to different owners or come from different sources. Malicious attackers may be able to infer confidential information even from aggregated numerical values returned as answers to queries over large collections of data. Formal proofs of security guarantees are important, because they can be used for implementing practical systems protecting privacy and providing answers to aggregated queries. The investigation of formal conditions which guarantee protection of private information against inference attacks originates from a fundamental result obtained by Chin and Ozsoyoglu in 1982 for linear queries. The present paper solves similar problems for two new classes of aggregated nonlinear queries. We obtain complete descriptions of conditions, which guarantee the protection of privacy of confidential information against certain possible inference attacks, if a collection of queries of this type are answered. Rigorous formal security proofs are given which guarantee that the conditions obtained ensure the preservation of privacy of confidential data. In addition, we give necessary and sufficient conditions for the protection of confidential information from special inference attacks aimed at achieving a group compromise.

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SEPSI: A Secure and Efficient Privacy-Preserving Set Intersection with Identity Authentication in IoT

Cited by 6 publications

References 43 publications

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

Future of AI-Driven IoT: Identifying Emerging Trends in Intelligent Data Analysis and Privacy Protection

The Future of AI in IoT: Emerging Trends in Intelligent Data Analysis and Privacy Protection

Protecting Private Information for Two Classes of Aggregated Database Queries

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