Privacy is All You Need: Revolutionizing Wearable Health Data with Advanced PETs
- URL: http://arxiv.org/abs/2503.03428v1
- Date: Wed, 05 Mar 2025 12:01:22 GMT
- Title: Privacy is All You Need: Revolutionizing Wearable Health Data with Advanced PETs
- Authors: Karthik Barma, Seshu Babu Barma,
- Abstract summary: We propose a Privacy-Enhancing Technology (PET) framework for wearable devices.<n>We integrate federated learning, lightweight cryptographic methods, and selectively deployed blockchain technology.<n>Our framework reduces privacy risks by up to 70 percent while preserving data utility and performance.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In a world where data is the new currency, wearable health devices offer unprecedented insights into daily life, continuously monitoring vital signs and metrics. However, this convenience raises privacy concerns, as these devices collect sensitive data that can be misused or breached. Traditional measures often fail due to real-time data processing needs and limited device power. Users also lack awareness and control over data sharing and usage. We propose a Privacy-Enhancing Technology (PET) framework for wearable devices, integrating federated learning, lightweight cryptographic methods, and selectively deployed blockchain technology. The blockchain acts as a secure ledger triggered only upon data transfer requests, granting users real-time notifications and control. By dismantling data monopolies, this approach returns data sovereignty to individuals. Through real-world applications like secure medical data sharing, privacy-preserving fitness tracking, and continuous health monitoring, our framework reduces privacy risks by up to 70 percent while preserving data utility and performance. This innovation sets a new benchmark for wearable privacy and can scale to broader IoT ecosystems, including smart homes and industry. As data continues to shape our digital landscape, our research underscores the critical need to maintain privacy and user control at the forefront of technological progress.
Related papers
- Improving Privacy-Preserving Techniques for Smart Grid using Lattice-based Cryptography [1.4856472820492366]
SPDBlock is a blockchain-based solution ensuring privacy, integrity, and resistance to attacks.
It detects and prosecutes malicious entities while efficiently handling multi-dimensional data transmission.
Performance tests reveal SPDBlock's superiority in communication and computational efficiency over traditional schemes.
arXiv Detail & Related papers (2024-04-17T19:51:52Z) - A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing.
Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data.
Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
arXiv Detail & Related papers (2023-09-27T14:38:16Z) - Blockchain-empowered Federated Learning for Healthcare Metaverses:
User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses.
We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
arXiv Detail & Related papers (2023-07-29T12:54:03Z) - FedBlockHealth: A Synergistic Approach to Privacy and Security in
IoT-Enabled Healthcare through Federated Learning and Blockchain [2.993954417409032]
The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety.
Traditional approaches need to ensure security and privacy while maintaining computational efficiency.
This paper proposes a novel hybrid approach combining federated learning and blockchain technology to provide a secure and privacy-preserved solution.
arXiv Detail & Related papers (2023-04-16T01:55:31Z) - Secure Multi-Party Computation based Privacy Preserving Data Analysis in
Healthcare IoT Systems [0.0]
Data transferred to the digital environment pose a threat of privacy leakage.
In this study, it is aimed to propose a model to handle the privacy problems based on federated learning.
Our proposed model presents an extensive privacy and data analysis and achieve high performance.
arXiv Detail & Related papers (2021-09-29T10:39:25Z) - FakeSafe: Human Level Data Protection by Disinformation Mapping using
Cycle-consistent Adversarial Network [4.987581730476023]
disinformation strategy can be adapted into data science to protect valuable private and sensitive data.
Huge amount of private data are being generated from personal devices such as smart phone and wearable.
Building a secure data transfer and storage infrastructure to preserving privacy is costly in most cases and there is always a concern of data security due to human errors.
We propose a method, named FakeSafe, to provide human level data protection using generative adversarial network with cycle consistency.
arXiv Detail & Related papers (2020-11-23T08:47:40Z) - Second layer data governance for permissioned blockchains: the privacy
management challenge [58.720142291102135]
In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data is crucial to avoid the massive infection and decrease the number of deaths.
In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts.
arXiv Detail & Related papers (2020-10-22T13:19:38Z) - BeeTrace: A Unified Platform for Secure Contact Tracing that Breaks Data
Silos [73.84437456144994]
Contact tracing is an important method to control the spread of an infectious disease such as COVID-19.
Current solutions do not utilize the huge volume of data stored in business databases and individual digital devices.
We propose BeeTrace, a unified platform that breaks data silos and deploys state-of-the-art cryptographic protocols to guarantee privacy goals.
arXiv Detail & Related papers (2020-07-05T10:33:45Z) - A vision for global privacy bridges: Technical and legal measures for
international data markets [77.34726150561087]
Despite data protection laws and an acknowledged right to privacy, trading personal information has become a business equated with "trading oil"
An open conflict is arising between business demands for data and a desire for privacy.
We propose and test a vision of a personal information market with privacy.
arXiv Detail & Related papers (2020-05-13T13:55:50Z) - Beyond privacy regulations: an ethical approach to data usage in
transportation [64.86110095869176]
We describe how Federated Machine Learning can be applied to the transportation sector.
We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
arXiv Detail & Related papers (2020-04-01T15:10:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.