Privacy-Preserving Data Management using Blockchains
- URL: http://arxiv.org/abs/2408.11263v1
- Date: Wed, 21 Aug 2024 01:10:39 GMT
- Title: Privacy-Preserving Data Management using Blockchains
- Authors: Michael Mireku Kwakye,
- Abstract summary: Data providers need to control and update existing privacy preferences due to changing data usage.
This paper proposes a blockchain-based methodology for preserving data providers private and sensitive data.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Privacy-preservation policies are guidelines formulated to protect data providers private data. Previous privacy-preservation methodologies have addressed privacy in which data are permanently stored in repositories and disconnected from changing data provider privacy preferences. This occurrence becomes evident as data moves to another data repository. Hence, the need for data providers to control and flexibly update their existing privacy preferences due to changing data usage continues to remain a problem. This paper proposes a blockchain-based methodology for preserving data providers private and sensitive data. The research proposes to tightly couple data providers private attribute data element to privacy preferences and data accessor data element into a privacy tuple. The implementation presents a framework of tightly-coupled relational database and blockchains. This delivers secure, tamper-resistant, and query-efficient platform for data management and query processing. The evaluation analysis from the implementation validates efficient query processing of privacy-aware queries on the privacy infrastructure.
Related papers
- FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation [4.772368796656325]
In practice, controlled data access remains a mainstream method for protecting data privacy in many industrial and research environments.
We developed the demo prototype FT-PrivacyScore to show that it's possible to efficiently and quantitatively estimate the privacy risk of participating in a model fine-tuning task.
arXiv Detail & Related papers (2024-10-30T02:41:26Z) - Protecting Data Buyer Privacy in Data Markets [27.092529976384238]
The paramount concern of privacy has predominantly centered on protecting privacy of data owners and third parties.
In this article, we address this gap by modeling the intricacies of data buyer privacy protection and investigating the delicate balance between privacy and purchase cost.
arXiv Detail & Related papers (2024-07-13T04:45:06Z) - Collection, usage and privacy of mobility data in the enterprise and public administrations [55.2480439325792]
Security measures such as anonymization are needed to protect individuals' privacy.
Within our study, we conducted expert interviews to gain insights into practices in the field.
We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy.
arXiv Detail & Related papers (2024-07-04T08:29:27Z) - Marking the Pace: A Blockchain-Enhanced Privacy-Traceable Strategy for Federated Recommender Systems [11.544642210389894]
Federated recommender systems have been enhanced through data sharing and continuous model updates.
Given the sensitivity of IoT data, transparent data processing in data sharing and model updates is paramount.
Existing methods fall short in tracing the flow of shared data and the evolution of model updates.
We present LIBERATE, a privacy-traceable federated recommender system.
arXiv Detail & Related papers (2024-06-07T07:21:21Z) - 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) - Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining [75.25943383604266]
We question whether the use of large Web-scraped datasets should be viewed as differential-privacy-preserving.
We caution that publicizing these models pretrained on Web data as "private" could lead to harm and erode the public's trust in differential privacy as a meaningful definition of privacy.
We conclude by discussing potential paths forward for the field of private learning, as public pretraining becomes more popular and powerful.
arXiv Detail & Related papers (2022-12-13T10:41:12Z) - How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
We study the connection between the per-subject norm in DP neural networks and individual privacy loss.
We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
arXiv Detail & Related papers (2022-11-18T11:39:03Z) - 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) - Utility-aware Privacy-preserving Data Releasing [7.462336024223669]
We propose a two-step perturbation-based privacy-preserving data releasing framework.
First, certain predefined privacy and utility problems are learned from the public domain data.
We then leverage the learned knowledge to precisely perturb the data owners' data into privatized data.
arXiv Detail & Related papers (2020-05-09T05:32:46Z) - A Quantum-based Database Query Scheme for Privacy Preservation in Cloud
Environment [7.331387596311974]
Privacy-preserving database query allows the user to retrieve a data item from the cloud database without revealing the information of the queried data item.
All the data items of the database are encrypted by different keys for protecting server's privacy.
Two oracle operations, a modified Grover iteration, and a special offset encryption mechanism are combined together to ensure that the client can correctly query the desirable data item.
arXiv Detail & Related papers (2020-02-01T11:14:38Z)
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.