A new privacy-preserving proximal support vector machine for classification of vertically partitioned data

Li, Sun; Mu, Weisong; Qi, Biao; Zhou, Zhijian

doi:10.1007/s13042-014-0245-1

Cited by 31 publications

(7 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, people have become interested in privacy-preserving classification and data mining [1][2][3][4][5][6][7][8][9][10] and have been involved in the field of optimization, especially in linear programming [11][12][13][14][15], where the data to be classified or mined belongs to different entities that are not willing to disclose the data. Mangasarian [13] proposed a random matrix which make the original linear programming problem into a secure linear programming problem.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs

Zhang

Kong

Pan

et al. 2021

Journal of Mathematics

View full text Add to dashboard Cite

In a linear programming for horizontally partitioned data, the equality constraint matrix is divided into groups of rows. Each group of the matrix rows and the corresponding right-hand side vector are owned by different entities, and these entities are reluctant to disclose their own groups of rows or right-hand side vectors. To calculate the optimal solution for the linear programming in this case, Mangasarian used a random matrix of full rank with probability 1, but an event with probability 1 is not a certain event, so a random matrix of full rank with probability 1 does not certainly happen. In this way, the solution of the original linear programming is not equal to the solution of the secure linear programming. We used an invertible random matrix for this shortcoming. The invertible random matrix converted the original linear programming problem to a secure linear program problem. This secure linear programming will not reveal any of the privately held data.

show abstract

Section: Introductionmentioning

confidence: 99%

“…e linear programming (1) Proof. As the matrix B is an m-order invertible matrix, the following relation holds:…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs

Zhang

Kong

Pan

et al. 2021

Journal of Mathematics

View full text Add to dashboard Cite

show abstract

“…Activity recognition forms the core of IoT based services, including health monitoring and the eldercare at smart homes [1][2][3]. Activity recognition based on the inertial sensors embedded in the smartphone has received classification algorithms, including PPSVM [8][9][10][11][12][13][14] PPKNN [15,16], privacy preserving logistic regression [6,17] and naive bays [18][19][20][21]. Another challenge for such sensor-based activity recognition task is the difference in the statistical distributions of training and testing data [22].…”

Section: Introductionmentioning

confidence: 99%

A privacy-preserving distributed transfer learning in activity recognition

et al. 2019

View full text Add to dashboard Cite

Along with the widespread use of smartphones, activity recognition using embedded inertial sensors has intrigued researchers. The learning and employing activity recognition systems suffers from some challenges. Training data of different persons should be involved in the learning procedure. In contrast, the activities of each individual should remain private. In addition, the style of different individuals activities makes the training phase become a challenging problem. This paper presents two privacy-preserving transfer learning algorithms for activity recognition application namely as P 2 S 2 TM and P 2 DS 2 TM. The P 2 S 2 TM algorithm is designed based on preserving data privacy by the client, while the P 2 DS 2 TM algorithm trusts the preservation of the data privacy by both the client and the server. The performance of the proposed algorithms is influenced by a random matrix. The performances of the algorithms are compared with those of the classic PPSVM classifier for different dimensions of the random matrix. The evaluation of the proposed algorithms revealed superior performance comparing with PPSVM in different scenarios. For the random matrix with the dimension of 500 × 561, The P 2 S 2 TM and P 2 DS 2 TM algorithms are able to relatively reduce privacy-preserving activity recognition error rate by 68% and 16% respectively; while preserving privacy with an acceptable rate.

show abstract

“…Due to law and privacy policies, the data cannot be pooled in one place. In these cases, solutions that provide data privacy are preferred instead of traditional data mining algorithms [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Outsourcing of Secure k-Nearest Neighbours Interpolation Method

Kalideen¹,

Tuğrul²

2018

ijacsa

View full text Add to dashboard Cite

Abstract-Cloud computing becomes essential in these days for the enterprises. Most of the large companies are moving their services and data to the cloud servers which offer flexibility and efficiency. Data owner (DO) hires a cloud service provider (CSP) to store its data and carry out the related computation. The query owner (QO) sends a request which is crucial for its future plans to the CSP. The CSP computes all necessary calculations and returns the result back to the QO. Neither the data nor query owners want to reveal their private data to anyone. k-Nearest Neighbour (k-NN) interpolation is one of the essential algorithms to produce a prediction value for an unmeasured location. Simply, it finds k number of nearest neighbours around the query point to produce an output. Oblivious RAM (ORAM) has been used to protect the privacy in cloud computing. In our work, we will perform the k-NN method using the kd-tree and ORAM without revealing both the data-owner's and query owner's confidential data to each other or to third parties. The proposed solution will be analysed to ensure that it provides accurate and reliable predictions while preserving the privacy of all parties.

show abstract

A new privacy-preserving proximal support vector machine for classification of vertically partitioned data

Cited by 31 publications

References 23 publications

A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs

A New Algorithm for Privacy-Preserving Horizontally Partitioned Linear Programs

A privacy-preserving distributed transfer learning in activity recognition

Outsourcing of Secure k-Nearest Neighbours Interpolation Method

Contact Info

Product

Resources

About