Research

Privacy-Preserving Sparse Vector Aggregation using Local Differential Privacy Mechanisms

By: Akshat Gaurav, Ronin Institute, USA

In the era of data-driven applications and analytics, the need to aggregate sensitive information while preserving privacy has become increasingly crucial. Sparse vector aggregation, which involves combining sparse vectors from multiple participants, is a common operation in various domains such as federated learning, distributed sensor networks, and collaborative data analysis. However, the aggregation process raises privacy concerns as individual vector elements may contain sensitive information. To address this challenge, local differential privacy (LDP) mechanisms have emerged as a promising approach to ensure privacy preservation during the aggregation process. In this article, we explore the concept of privacy-preserving sparse vector aggregation using local differential privacy mechanisms and discuss its benefits and implementation considerations.

Understanding Local Differential Privacy (LDP)

Local differential privacy provides a framework for privacy-preserving data analysis where participants inject random noise into their local data before sharing aggregated results. By perturbing individual data elements with carefully calibrated noise, LDP ensures that no specific participant’s information is revealed while still enabling meaningful analysis at the aggregate level. LDP achieves a strong privacy guarantee by adding noise directly at the source without requiring a trusted third party.

Privacy-Preserving Sparse Vector Aggregation with LDP

Sparse vector aggregation poses unique challenges due to the presence of zero or missing elements, which can leak information about the participating users. To preserve privacy in sparse vector aggregation, local differential privacy mechanisms can be applied, allowing participants to independently perturb their vector elements while guaranteeing privacy at the aggregate level.

Key Steps in Privacy-Preserving Sparse Vector Aggregation using LDP

  1. Data Perturbation: Each participant perturbs their sparse vector by injecting random noise. The noise is carefully calibrated to satisfy the privacy requirements of local differential privacy.
  2. Aggregation: Perturbed vectors from all participants are aggregated to compute the final aggregated vector. This aggregation process typically involves combining the non-zero elements from different participants while handling the zero or missing elements appropriately.
  3. Noise Calibration: The noise injected by each participant is determined based on the privacy parameters of local differential privacy, such as privacy budget and sensitivity of the data. Careful calibration ensures that the aggregated vector maintains privacy guarantees while minimizing the impact on utility.

Benefits of Privacy-Preserving Sparse Vector Aggregation using LDP

  1. Strong Privacy Guarantee: Local differential privacy provides a rigorous privacy guarantee, ensuring that individual vector elements and the presence or absence of values remain protected throughout the aggregation process.
  2. Data Control: Participants retain control over their local data and independently inject noise, minimizing trust requirements and reducing the risk of information leakage.
  3. Collaboration without Compromise: Privacy-preserving sparse vector aggregation enables collaborative analysis and data sharing while maintaining privacy, allowing organizations to leverage collective intelligence without compromising sensitive information.

Implementation Considerations

Implementing privacy-preserving sparse vector aggregation using local differential privacy requires careful consideration of several factors:

  1. Privacy Budget: Determining the privacy budget and allocating it appropriately among participants is crucial to maintain privacy guarantees. Overspending the privacy budget may lead to privacy breaches, while underspending may result in inadequate privacy protection.
  2. Noise Calibration: Accurate calibration of noise parameters is essential to balance privacy and utility. Noise should be carefully selected to provide privacy protection while preserving the integrity and quality of the aggregated vector.
  3. Trade-off between Privacy and Utility: As with any privacy-preserving mechanism, there is a trade-off between privacy and utility. Aggregation algorithms and noise injection strategies should be designed to strike an optimal balance between privacy preservation and the usefulness of the aggregated results.

Table1: Privacy-Preserving Sparse Vector Aggregation Workflow

StepDescription
Data PerturbationParticipants independently perturb their sparse vectors using local differential privacy mechanisms.
AggregationPerturbed vectors from all participants are aggregated to compute the final aggregated vector.
Noise CalibrationNoise parameters, such as privacy budget and sensitivity, are carefully calibrated to maintain privacy while minimizing utility loss.

Conclusion

Privacy-preserving sparse vector aggregation using local differential privacy mechanisms offers a promising approach to address privacy concerns while aggregating sensitive information. By leveraging local noise injection and careful aggregation techniques, individual privacy is preserved, allowing collaborative analysis without compromising data confidentiality. However, successful implementation requires proper calibration of noise parameters, privacy budget management, and striking the right balance between privacy and utility. As data-driven applications continue to evolve, privacy-preserving techniques like LDP will play a pivotal role in ensuring secure and privacy-aware data aggregation, enabling organizations to unlock the full potential of collaborative analysis while respecting data privacy.

References

  1. Fu, N., Ni, W., Hu, H., & Zhang, S. (2023). Multidimensional grid-based clustering with local differential privacy. Information Sciences, 623, 402-420.
  2. Tran-Truong, P. T., & Dang, T. K. (2022, November). pPATE: A Pragmatic Private Aggregation of Teacher Ensembles Framework by Sparse Vector Technique Based Differential Privacy, Paillier Cryptosystem and Human-in-the-loop. In International Conference on Future Data and Security Engineering (pp. 332-346). Singapore: Springer Nature Singapore.
  3. Zheng, X., Guan, M., Jia, X., Guo, L., & Luo, Y. (2022). A Matrix Factorization Recommendation System-Based Local Differential Privacy for Protecting Users’ Sensitive Data. IEEE Transactions on Computational Social Systems.
  4. Gupta, B. B., Joshi, R. C., & Misra, M. (2012). ANN based scheme to predict number of zombies in a DDoS attack. Int. J. Netw. Secur., 14(2), 61-70.
  5. Horvath, A. N., Berchier, M., Nooralahzadeh, F., Allam, A., & Krauthammer, M. (2023). Exploratory Analysis of Federated Learning Methods with Differential Privacy on MIMIC-III. arXiv preprint arXiv:2302.04208.
  6. Dahiya, A., et al. (2021). A reputation score policy and Bayesian game theory based incentivized mechanism for DDoS attacks mitigation and cyber defense. Future Generation Computer Systems, 117, 193-204.
  7. Wang, Y., Gao, M., Ran, X., Ma, J., & Zhang, L. Y. (2023). An improved matrix factorization with local differential privacy based on piecewise mechanism for recommendation systems. Expert Systems with Applications, 216, 119457.
  8. Sahoo, S. R., et al. (2019). Hybrid approach for detection of malicious profiles in twitter. Computers & Electrical Engineering, 76, 65-81.
  9. Zhang, P., Cheng, X., Su, S., & Wang, N. (2023). Effective truth discovery under local differential privacy by leveraging noise-aware probabilistic estimation and fusion. Knowledge-Based Systems, 261, 110213.
  10. Cvitić, I., et al. (2021). Boosting-based DDoS detection in internet of things systems. IEEE Internet of Things Journal, 9(3), 2109-2123.
  11. Xu, C., Mei, X., Liu, D., Zhao, K., & Ding, A. S. (2022). An efficient privacy-preserving point-of-interest recommendation model based on local differential privacy. Complex & Intelligent Systems, 1-24.
  12. Gupta, B. B., Yadav, K., Razzak, I., Psannis, K., Castiglione, A., & Chang, X. (2021). A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment. Computer Communications, 175, 47-57.
  13. Zhou, M., Wang, T., Chan, T. H., Fanti, G., & Shi, E. (2022, May). Locally differentially private sparse vector aggregation. In 2022 IEEE Symposium on Security and Privacy (SP) (pp. 422-439). IEEE.
  14. Gupta, S., et al. (2018). Hunting for DOM-Based XSS vulnerabilities in mobile cloud-based online social network. Future Generation Computer Systems, 79, 319-336.

Cite as:

Gaurav A (2023) Privacy-Preserving Sparse Vector Aggregation using Local Differential Privacy Mechanisms, Insights2Techinfo, pp.1

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