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#5042. (k, ?, ?)-Anonymization: privacy-preserving data release based on k-anonymity and differential privacy
| July 2026 | publication date |
| Proposal available till | 28-05-2025 |
| 4 total number of authors per manuscript | 0 $ |
| The title of the journal is available only for the authors who have already paid for |
| Journal’s subject area: |
| Management Information Systems; Information Systems; Hardware and Architecture; Software; |
| place 1 | place 2 | place 3 | place 4 |
| Free | Free | Free | Free |
| 2350 $ | 1200 $ | 1050 $ | 900 $ |
Contract №5042.1   | Contract №5042.2 | Contract №5042.3 | Contract №5042.4 |
1 place - free (for sale)
2 place - free (for sale)
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4 place - free (for sale)
Abstract:
The General Data Protection Regulation came into effect on May 25, 20XX, and has rapidly become a touchstone model for modern privacy law. However, new guarantees of consumer privacy adversely affect data sharing and data application markets because service companies (e.g., Apple, Google, Microsoft) cannot provide immediate and optimized services through analysis of collected consumer experiences. Various workarounds based on existing methods such as k-anonymity and differential privacy technologies have been proposed. However, they are limited in data utility, and their data sets have high dimensionality (the so-called curse of dimensionality). In this paper, we propose the (k,ε,δ)-anonymization synthetic data set generation mechanism to protect data privacy before releasing data sets to be analyzed. Synthetic data sets satisfy the definitions of k-anonymity and differential privacy by applying KD-tree and random sampling mechanisms. Moreover, (k,ε,δ)-anonymization uses principle component analysis to rationally replace high-dimensional data sets with lower-dimensional data sets for consideration of efficient computation. We report a privacy analysis and a series of experiments that prove that (k,ε,δ)-anonymization is feasible and efficient.
Keywords:
Data privacy; Differential privacy; k-anonymity; Synthetic data set
Contacts :
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)
Abstract:
The General Data Protection Regulation came into effect on May 25, 20XX, and has rapidly become a touchstone model for modern privacy law. However, new guarantees of consumer privacy adversely affect data sharing and data application markets because service companies (e.g., Apple, Google, Microsoft) cannot provide immediate and optimized services through analysis of collected consumer experiences. Various workarounds based on existing methods such as k-anonymity and differential privacy technologies have been proposed. However, they are limited in data utility, and their data sets have high dimensionality (the so-called curse of dimensionality). In this paper, we propose the (k,ε,δ)-anonymization synthetic data set generation mechanism to protect data privacy before releasing data sets to be analyzed. Synthetic data sets satisfy the definitions of k-anonymity and differential privacy by applying KD-tree and random sampling mechanisms. Moreover, (k,ε,δ)-anonymization uses principle component analysis to rationally replace high-dimensional data sets with lower-dimensional data sets for consideration of efficient computation. We report a privacy analysis and a series of experiments that prove that (k,ε,δ)-anonymization is feasible and efficient.
Keywords:
Data privacy; Differential privacy; k-anonymity; Synthetic data set
Contacts :
