Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors

Keskeiset käsitteet

Homomorphic encryption enables privacy-preserving anomaly detection on IoT data without decryption.

Tiivistelmä

The article discusses the challenges of anomaly detection on encrypted IoT data and proposes a novel solution using homomorphic encryption. It introduces a Histogram-based technique for detecting anomalies without decryption, addressing limitations related to input size and computation depth. The proposed method efficiently detects abnormal values while maintaining privacy and achieving consistent results comparable to plain data analysis. Evaluation results demonstrate robustness against various challenges in IoT environments.

Tilastot

Encryption can mitigate vulnerability to malfunctions or cyberattacks.
Homomorphic encryption schemes enable processing operations on encrypted IoT data.
Evaluation results show effective anomaly detection without decryption.
Computational overheads are reasonable compared to existing literature.

Lainaukset

"Encryption can mitigate these challenges; however, most existing anomaly detection techniques decrypt the data to perform the analysis."
"Homomorphic encryption schemes are promising solutions as they enable the processing and execution of operations on IoT data while still encrypted."
"Evaluation results show that the solution effectively detects anomalies without requiring IoT data decryption."

Tärkeimmät oivallukset

Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors

by Anca Hangan,... klo arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09322.pdf

Syvällisempiä Kysymyksiä

How can homomorphic encryption impact other areas beyond anomaly detection

Homomorphic encryption can have a significant impact beyond anomaly detection in various areas. One key area is secure data sharing and collaboration, especially in industries like healthcare and finance where sensitive information needs to be analyzed without compromising privacy. By enabling computations on encrypted data, homomorphic encryption allows multiple parties to perform joint analysis on their combined datasets without revealing the underlying raw data. This can lead to advancements in collaborative research, fraud detection, personalized medicine, and more while maintaining data privacy.
Another area where homomorphic encryption can make a difference is in cloud computing. It enables users to securely outsource their computation tasks to the cloud while keeping their data encrypted throughout the process. This enhances privacy protection for individuals or organizations utilizing cloud services by ensuring that sensitive information remains confidential even during processing.
Furthermore, homomorphic encryption has implications for secure machine learning models deployment. With this technology, machine learning algorithms can operate directly on encrypted data sources without needing access to plaintext information. This capability opens up possibilities for deploying AI models in scenarios where data confidentiality is paramount, such as predictive maintenance systems or personalized recommendations based on private user preferences.

What are potential drawbacks or limitations of relying solely on homomorphic encryption for privacy preservation

While homomorphic encryption offers strong privacy guarantees by allowing computations on encrypted data, there are some potential drawbacks and limitations associated with relying solely on this technique for privacy preservation:

Computational Overhead: Homomorphic encryption incurs significant computational overhead due to the complex mathematical operations involved in processing encrypted data. This overhead can impact system performance and response times, making it challenging to scale applications using homomorphic encryption for real-time processing of large datasets.

Limited Functionality: Most existing fully homomorphic encryption schemes support only basic arithmetic operations like addition and multiplication efficiently but struggle with more complex operations like comparison or division. This limitation restricts the types of computations that can be performed securely on encrypted data.

Key Management Complexity: Managing keys for homomorphic encryption systems can be complex and cumbersome, especially when dealing with multiple parties collaborating on shared datasets or when rotating keys regularly for security purposes.

4 .Security Risks: While homomorphic encryption protects against unauthorized access to plaintext data during computation, it does not address all security risks such as side-channel attacks or implementation vulnerabilities that could potentially compromise the integrity of the system.
5 .Scalability Challenges: Scaling up applications using homomorphically encrypted techniques may pose challenges related to interoperability with existing systems, integration complexity with different platforms or technologies.

How might advancements in AI technologies influence the effectiveness of privacy-preserving techniques like homomorphic encryption

Advancements in AI technologies have the potential to both enhance and influence the effectiveness of privacy-preserving techniques like homomorphic encryption:
1 .Improved Efficiency: As AI algorithms become more sophisticated and efficient at handling large volumes of complex data sets through techniques like deep learning neural networks or reinforcement learning models they may require increased computational power which could benefit from enhanced methods of preserving privacy such as those provided by advanced forms of cryptographic protocols including fully-homorphic encryptions
2 .Enhanced Security Measures: The integration of AI into cybersecurity frameworks could bolster defenses against emerging threats by leveraging advanced analytics capabilities powered by machine learning algorithms operating over protected (encrypted) datasets via mechanisms such as differential privacies
3 .Privacy-Preserving Machine Learning: Advancements in federated learning approaches enabled by AI technologies allow training ML models across distributed devices while preserving individual user's private information through secure aggregation protocols thus enhancing overall model accuracy without exposing raw personal details
4 Interdisciplinary Collaboration: The synergy between AI researchers specializingin developing cutting-edge ML/DL solutionsand cryptography experts focusingon advancingprivacy-enhancingtechniqueslikehomomorphicschemescouldleadtoinnovativeapproachesthatleveragebothfields' strengthsforcomprehensiveprivacyprotectionwhileenablingadvanceddataanalysiscapabilities