Efficient Unconditionally Secure Comparison and Privacy Preserving Machine Learning Classification Protocols

David, Bernardo; Dowsley, Rafael; Katti, R.; Nascimento, Anderson C. A.

doi:10.1007/978-3-319-26059-4_20

Cited by 24 publications

(24 citation statements)

References 40 publications

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“…Non-application specific protocols were designed just lately. Bost et al [17] introduced privacypreserving protocols for hyperplane-based, Naive Bayes and DT classifiers, Wu et al [18] for DTs and RFs, David et al [19] for hyperplane-based and Naive Bayes classifiers, and De Cock et al [20] for DTs and hyperplane-based classifiers. De Hoogh et al [14] had also previously presented a protocol for privacy-preserving scoring of DTs with categorical attributes.…”

Section: Related Workmentioning

confidence: 99%

“…The commodity-based model allows the realization of non-trivial functionality in the UC framework and has already been used to get very efficient secure computation protocols for tasks such as computing inner-products [29], [30] and other linear algebra operations [31], string equality [30], set intersection [30], oblivious polynomial evaluation [32] and verifiable secret sharing [33]. It was used in protocols for PPML [15], [19], [20]. In practice, this correlated randomness can be distributed by: (1) a single trusted server, (2) many not completely trusted servers (only a majority of honest servers is necessary [28]), or (3) pre-computed by the parties in an offline phase using an SMC protocol to emulate the trusted initializer (in this case the advantage is in offloading the heavy computation to be run at any idle time).…”

Section: B Cryptographic Preliminaries and Building Blocksmentioning

confidence: 99%

See 1 more Smart Citation

Privacy-Preserving Scoring of Tree Ensembles: A Novel Framework for AI in Healthcare

Fritchman

Saminathan

Dowsley

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Machine Learning (ML) techniques now impact a wide variety of domains. Highly regulated industries such as healthcare and finance have stringent compliance and data governance policies around data sharing. Advances in secure multiparty computation (SMC) for privacy-preserving machine learning (PPML) can help transform these regulated industries by allowing ML computations over encrypted data with personally identifiable information (PII). Yet very little of SMC-based PPML has been put into practice so far. In this paper we present the very first framework for privacypreserving classification of tree ensembles with application in healthcare. We first describe the underlying cryptographic protocols that enable a healthcare organization to send encrypted data securely to a ML scoring service and obtain encrypted class labels without the scoring service actually seeing that input in the clear. We then describe the deployment challenges we solved to integrate these protocols in a cloud based scalable risk-prediction platform with multiple ML models for healthcare AI. Included are system internals, and evaluations of our deployment for supporting physicians to drive better clinical outcomes in an accurate, scalable, and provably secure manner. To the best of our knowledge, this is the first such applied framework with SMC-based privacypreserving machine learning for healthcare.

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Section: Related Workmentioning

confidence: 99%

Section: B Cryptographic Preliminaries and Building Blocksmentioning

confidence: 99%

Privacy-Preserving Scoring of Tree Ensembles: A Novel Framework for AI in Healthcare

Fritchman

Saminathan

Dowsley

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

show abstract

“…After preprocessing and feature extraction, we have 318,562 valid instances remaining in the set. The set is divided into 4 similar-sized age groups: (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26), (27)(28)(29)(30)(31)(32)(33)(34), (35)(36)(37)(38)(39)(40)(41)(42)(43), . For age classification, each instance will be classified into one age bucket.…”

Section: A Age and Gender Classificationmentioning

confidence: 99%

“…Cryptographically secure privacy-preserving SVM classification protocols have been proposed in [33], [5], [16], [10]. The basic idea behind these protocols is to decompose the task of scoring an SVM into smaller tasks and to implement each one of them in a privacy-preserving way.…”

Section: Adding Privacy To Our Classifiersmentioning

confidence: 99%

“…These operations are usually expensive from a computational complexity point of view, demanding costly modular exponentiations. In [16], [10], highly efficient protocols for privacy-preserving comparison and argmax in the commodity-based model [4] were proposed. In the commodity-based model correlated data is pre-distributed to the parties by a trusted initializer during an off-line setup phase.…”

Section: Adding Privacy To Our Classifiersmentioning

confidence: 99%

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VirtualIdentity: Privacy preserving user profiling

Wang

Poon

Farnadi

et al. 2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Abstract-User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling -which are the intellectual property of the company -to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their usergenerated text and personal pictures with trained support vector machine models in a privacy preserving manner.

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Privacy‐preserving convolutional neural network prediction with low latency and lightweight users

Chen

Duan

et al. 2021

Int J Intell Syst

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Convolutional neural networks (CNNs) have excellent and extensive applications in image recognition. With the continuous exploitation of data value and the proliferation of machine learning‐as‐a‐service, convolutional neural network prediction schemes on privacy preservation have been introduced one after another, which makes much more attention focused on the privacy leakage and services offered to be efficient and light. Therefore, how to improve the convolutional neural prediction scheme on the premise of privacy preservation turns out to be an imperative research issue. In this paper, we propose a privacy‐preserving convolutional neural network prediction scheme (PCP‐LL) that supports low latency and lightweight users. The scheme starts from the perspective of lossless accuracy from underlying networks. First, we construct a secure activation function computing protocol (SActF) utilizing a commodity‐based secure comparison protocol, which reduces the complexity and latency during the activation function computing under ciphertexts compared with common schemes. Second, to further support lightweight users, we introduce a secure output layer protocol (SOut) that enables users to obtain the prediction results without extra decryption after simple operations. Then, the scheme adopts the distributed two trapdoors public‐key cryptosystem (DT‐PKC) to achieve both data and model security, which well avoids security issues especially such as wiretapping by semi‐honest participants commonly in secret sharing schemes. Finally, through relevant evaluations, the scheme not only achieves privacy preservation and low latency, but also supports lightweight users.

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Efficient Unconditionally Secure Comparison and Privacy Preserving Machine Learning Classification Protocols

Cited by 24 publications

References 40 publications

Privacy-Preserving Scoring of Tree Ensembles: A Novel Framework for AI in Healthcare

Privacy-Preserving Scoring of Tree Ensembles: A Novel Framework for AI in Healthcare

VirtualIdentity: Privacy preserving user profiling

Privacy‐preserving convolutional neural network prediction with low latency and lightweight users

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