Privacy-Preserving IoT Data Aggregation in Adversarial Environments

Anshul Goel; Tejaskumar Pujari

doi:10.36948/ijfmr.2023.v05i06.42006

Privacy-Preserving IoT Data Aggregation in Adversarial Environments

Author(s)	Anshul Goel, Tejaskumar Pujari
Country	India
Abstract	IoT devices become privacy risks no matter they use central or distributed data processing because they handle many personal user data. When data collection occurs in compromised settings hackers can either change or steal important data items. Research on secure IoT data aggregation keeps being essential because it defends privacy while making attacks harder. This study seeks new methods to secure user privacy during data combining while preventing untrusted parties from tampering with information. Our objective is to make consistent IoT systems workable in risky areas by resolving present field difficulties. In dangerous digital settings IoT security threats involve both unauthorized intrusion and malicious hacking alongside unauthorized data access. These security weaknesses damage our database structure making it easy for unauthorized users to take private pieces of information. The research suggests using homomorphic encryption and secure multi-party computation to process data since both techniques protect sensitive information during computation. To address IoT system security problems our proposal combines these advanced methods which defend privacy and enable safe data examinations. This research examines both standards governing IoT data aggregation as well as its ethical ramifications. Organizations need to change their data handling practices when user privacy rules develop. Installing privacy tools helps organizations follow regulations and builds better overall safety for their IoT systems. This analysis explores existing and future IoT approaches for privacy that shows spent ways to secure sensitive data against attacks.
Keywords	Iot, Devices, Sensitive Data, Data Aggregation, Centralized Systems, Decentralized Systems, Privacy Risks, Adversarial Environments, Attackers, Manipulate, Extract Information, Secure Aggregation, Privacy-Preserving Techniques, Robustness, Cryptographic Methods, Homomorphic Encryption, Secure Multi-Party Computation, Data Integrity, Vulnerabilities, Data Breaches, Unauthorized Access, Malicious Attacks, Confidentiality, Reliable Analysis, Regulatory Frameworks, Ethical Considerations, Data Management, Legal Standards, User Trust, Security Posture, Robust Solutions
Field	Engineering
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-11-07
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.42006
Short DOI	https://doi.org/g9f4rw

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Privacy-Preserving IoT Data Aggregation in Adversarial Environments

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