A decision-support framework for data anonymization with application to machine learning processes

Caruccio, Loredana; Desiato, Domenico; Polese, Giuseppe; Tortora, Genoveffa; Zannone, Nicola

doi:10.1016/j.ins.2022.09.004

Cited by 26 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the concept of data anonymization in mind, Ref. [21] proposed a strategy by decreasing the correlation between data and the identities. However, the utility of the data will be affected.…”

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Federated Singular Value Decomposition

2023

View full text Add to dashboard Cite

Singular value decomposition (SVD) is a fundamental technique widely used in various applications, such as recommendation systems and principal component analyses. In recent years, the need for privacy-preserving computations has been increasing constantly, which concerns SVD as well. Federated SVD has emerged as a promising approach that enables collaborative SVD computation without sharing raw data. However, existing federated approaches still need improvements regarding privacy guarantees and utility preservation. This paper moves a step further towards these directions: we propose two enhanced federated SVD schemes focusing on utility and privacy, respectively. Using a recommendation system use-case with real-world data, we demonstrate that our schemes outperform the state-of-the-art federated SVD solution. Our utility-enhanced scheme (utilizing secure aggregation) improves the final utility and the convergence speed by more than 2.5 times compared with the existing state-of-the-art approach. In contrast, our privacy-enhancing scheme (utilizing differential privacy) provides more robust privacy protection while improving the same aspect by more than 25%.

show abstract

Section: Related Workmentioning

confidence: 99%

Privacy-Preserving Federated Singular Value Decomposition

2023

View full text Add to dashboard Cite

show abstract

“…This paper adopts secure aggregation protocols based on local model masking among many types of methods rather than centralized learning methods: the methods based on data processing like data anonymization [4] and differential privacy [5], Secure Multi-Party Computation [6] and TEE [7]. The reason for not choosing these methods is that accuracy and low computational complexity are not easy to be simultaneously achieved.…”

Section: Related Workmentioning

confidence: 99%

“…First, users add some noises to their training data or anonymize them, in data processing based methods [4], [5]. This method prevents the server from seeing users ' training The associate editor coordinating the review of this manuscript and approving it for publication was Mehul S. Raval .…”

Section: Introductionmentioning

confidence: 99%

Byzantine-Resilient Secure Federated Learning on Low-Bandwidth Networks

et al. 2023

View full text Add to dashboard Cite

Privacy-preserving and Byzantine-resilient machine learning has been an important research issue, and many centralized methods have been developed. However, it is difficult for these methods to achieve fast learning and high accuracy simultaneously. In contrast, federated learning based on local model masking like Byzantine-Resilient Secure Aggregation (BREA), is a promising approach to simultaneously achieve them. Despite the advantage of light computation of randomizing local models of users for privacy preservation, the verification of shares generated from local models in BREA, which mitigates Byzantine attacks, still incurs large complexity in communication. The paper designs a share verification method for BREA to offload some parts of the share verification process from users to a semi-honest server, which avoids broadcasting large-size commitments to shares. In addition, to mitigate the increase in computation time due to computations offloaded to the server, our method makes the verification algorithm running on the server efficient and executes the server and user computations in parallel. In our experiments, our method provides a speedup of up to 15× on low-bandwidth networks like mobile networks. Our method also preserves BREA's resilience against Byzantine attacks.

show abstract

“…The published datasets typically contain large amounts of multi-dimensional sensitive data. That is to say, each individual has multiple sensitive information (e.g., shopping habits, medical history, and driving records) [ 4 ]. Attackers can use public data to analyze sensitive personal information, which may result in privacy disclosure.…”

Section: Introductionmentioning

confidence: 99%

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

Huang

Miao

et al. 2023

Sensors

View full text Add to dashboard Cite

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution’s difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks.

show abstract

A decision-support framework for data anonymization with application to machine learning processes

Cited by 26 publications

References 49 publications

Privacy-Preserving Federated Singular Value Decomposition

Privacy-Preserving Federated Singular Value Decomposition

Byzantine-Resilient Secure Federated Learning on Low-Bandwidth Networks

K-Anonymity Privacy Protection Algorithm for Multi-Dimensional Data against Skewness and Similarity Attacks

Contact Info

Product

Resources

About