A Semi-Supervised Learning Approach to Differential Privacy

Jagannathan, Geetha; Monteleoni, Claire; Pillaipakkamnatt, Krishnan

doi:10.1109/icdmw.2013.131

Cited by 17 publications

(15 citation statements)

References 12 publications

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“…According to [36], a distance-recoverable protocol is not secure against malicious adversaries, which makes the method in [35] only suitable against a semi-honest adversary. Reference [37] assumed a scenario where additional nonprivate data are available and provided a learning model to improve the accuracy of a differential-private classifier using both private and non-private data. Reference [38] also proposed a semi-supervised learning method for differential privacy.…”

Section: Related Workmentioning

confidence: 99%

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Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning

Yang

2020

IEEE Access

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Privacy has become a major concern in data mining as it is utilized in many important applications. Distributed privacy-preserving data mining (DPPDM) is one of the techniques to address this concern, which focuses on protecting private information of members in distributed systems during data mining. As DPPDM is widely discussed in recent works, the semi-supervised manner of learning still draws less attention in this field. In this paper, a mixture-model-based semi-supervised DPPDM method is proposed. By introducing our method, a site in a distributed system is able to initiate a learning process using labeled data of its own and unlabeled data from all the sites. During the process, no individual data of any site is revealed to others, no information about data can be traced back to any specific site, and only the initiating site learns the result. We propose a parameter-masking privacy-preserving Expectation-Maximization (EM) algorithm and a mixture-model-based semi-supervised learning algorithm as the two main steps of our method. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed method. INDEX TERMS Data privacy, distributed computing, expectation-maximization algorithms, graph theory, semisupervised learning. • The objective of the classifier. Different groups of categories are allowed to be introduced to label the same data

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Section: Related Workmentioning

confidence: 99%

“…Reference [38] also proposed a semi-supervised learning method for differential privacy. Like any other method for differential privacy, [37] and [38] focus on private information revealed by learning results and are not suitable for preserving data privacy against malicious collusion.…”

Section: Related Workmentioning

confidence: 99%

Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning

Yang

2020

IEEE Access

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“…They observed that this algorithm performs better than the differentially private ID3 tree in terms of accuracy values even for small datasets. In 2013, they have proposed a variant of the differentially private random tree ensemble in [22]. In this study, a semi-supervised method which modifies the random decision tree approach to be used with the unlabelled data has been performed.…”

Section: Classification With Differential Privacymentioning

confidence: 99%

A hybrid approach of homomorphic encryption and differential privacy for privacy preserving classification

Zorarpacı¹,

Özel²

2020

International Journal of Applied Mathematics Electronics and Computers

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Privacy preserving data mining is a substantial research area that aims at protecting the privacy of individuals while enabling to perform data mining techniques. In this study, we propose a secure protocol that fulfils the privacy restriction by combining homomorphic encryption with differential privacy and integrate this protocol into Holte's One Rule which is a simple, but accurate and efficient classification algorithm. The proposed method allows a researcher to get the answers of his/her queries to build One Rule classifier by processing the encrypted training dataset under Paillier's cryptosystem and also applies differential privacy to minimize the privacy leakage of individuals as much as possible in this training dataset. Therefore, both of security and privacy of the individuals in the training dataset for classification are provided thanks to our proposed method; since neither the parties, nor the researcher attain any information about the individuals in the database. Besides the One Rule classifier, we apply our proposed privacy preservation model to Naïve Bayes classification algorithm for the performance comparison, and show the efficiency of the proposed method through experiments on real data from UCI repository.

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“…The utility-based partitioning is inspired by an observation that many DP machine learning algorithms (e.g. [5, 7, 9, 19]) have their performance related with n , ε for a dataset of n records with ε -DP. We give definition of utility-based partitioning below.…”

Section: Partitioning Mechanismsmentioning

confidence: 99%

Partitioning-Based Mechanisms Under Personalized Differential Privacy

Xiong

et al. 2017

Lecture Notes in Computer Science

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Differential privacy has recently emerged in private statistical aggregate analysis as one of the strongest privacy guarantees. A limitation of the model is that it provides the same privacy protection for all individuals in the database. However, it is common that data owners may have different privacy preferences for their data. Consequently, a global differential privacy parameter may provide excessive privacy protection for some users, while insufficient for others. In this paper, we propose two partitioning-based mechanisms, privacy-aware and utility-based partitioning, to handle personalized differential privacy parameters for each individual in a dataset while maximizing utility of the differentially private computation. The privacy-aware partitioning is to minimize the privacy budget waste, while utility-based partitioning is to maximize the utility for a given aggregate analysis. We also develop a t-round partitioning to take full advantage of remaining privacy budgets. Extensive experiments using real datasets show the effectiveness of our partitioning mechanisms.

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A Semi-Supervised Learning Approach to Differential Privacy

Cited by 17 publications

References 12 publications

Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning

Mixture-Model-Based Graph for Privacy-Preserving Semi-Supervised Learning

A hybrid approach of homomorphic encryption and differential privacy for privacy preserving classification

Partitioning-Based Mechanisms Under Personalized Differential Privacy

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