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A large amount of data is often needed to train machine learning algorithms with confidence. One way to achieve the necessary data volume is to share and combine data from multiple parties. On the other hand, how to protect sensitive personal information during data sharing is always a challenge. We focus on data sharing when parties have overlapping attributes but non-overlapping individuals. One approach to achieve privacy protection is through sharing differentially private synthetic data. Each party generates synthetic data at its own preferred privacy budget, which is then released and horizontally merged across the parties. The total privacy cost for this approach is capped at the maximum individual budget employed by a party. We derive the mean squared error bounds for the parameter estimation in common regression analysis based on the merged sanitized data across parties. We identify through theoretical analysis the conditions under which the utility of sharing and merging sanitized data outweighs the perturbation introduced for satisfying differential privacy and surpasses that based on individual party data. The experiments suggest that sanitized HOMM data obtained at a practically reasonable small privacy cost can lead to smaller prediction and estimation errors than individual parties, demonstrating the benefits of data sharing while protecting privacy.
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Use & Abuse of Personal Information, Part II: Robust Generation of Fake IDs for Privacy Experimentation
When personal information is shared across the Internet, we have limited confidence that the designated second party will safeguard it as we would prefer. Privacy policies offer insight into the best practices and intent of the organization, yet most are written so loosely that sharing with undefined third parties is to be anticipated. Tracking these sharing behaviors and identifying the source of unwanted content is exceedingly difficult when personal information is shared with multiple such second parties. This paper formulates a model for realistic fake identities, constructs a robust fake identity generator, and outlines management methods targeted towards online transactions (email, phone, text) that pass both cursory machine and human examination for use in personal privacy experimentation. This fake ID generator, combined with a custom account signup engine, are the core front-end components of our larger Use and Abuse of Personal Information system that performs one-time transactions that, similar to a cryptographic one-time pad, ensure that we can attribute the sharing back to the single one-time transaction and/or specific second party. The flexibility and richness of the fake IDs also serve as a foundational set of control variables for a wide range of social science research questions revolving around personal information. Collectively, these fake identity models address multiple inter-disciplinary areas of common interest and serve as a foundation for eliciting and quantifying personal information-sharing behaviors.
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- Award ID(s):
- 1946493
- PAR ID:
- 10595273
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Journal of Cybersecurity and Privacy
- Volume:
- 4
- Issue:
- 3
- ISSN:
- 2624-800X
- Page Range / eLocation ID:
- 546 to 571
- Subject(s) / Keyword(s):
- fake identity privacy active OSINT open source intelligence
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/jcp4030026
