Privacy-preserving PCA on horizontally-partitioned data

Al-Rubaie, Mohammad; Wu, Pei-Yuan; Chang, J. Morris; Kung, Sun‐Yuan

doi:10.1109/desec.2017.8073817

Cited by 21 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many similar PCA techniques rely on results derived from random matrix theory [ 216 - 219 ]. To reduce the computational cost of the privacy model, additive HE was used for PCA with a single data user [ 217 ], where the rank of PCA with an unknown distribution could be adaptively estimated to achieve (𝜖, 𝛿)-DP [ 218 ]. More recently, the concept of collaborative learning (or shared machine learning) [ 94 , 97 , 220 ] became very popular in data anonymization.…”

Section: Resultsmentioning

confidence: 99%

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

Zuo¹,

Watson²,

Budgen³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

Background Data science offers an unparalleled opportunity to identify new insights into many aspects of human life with recent advances in health care. Using data science in digital health raises significant challenges regarding data privacy, transparency, and trustworthiness. Recent regulations enforce the need for a clear legal basis for collecting, processing, and sharing data, for example, the European Union’s General Data Protection Regulation (2016) and the United Kingdom’s Data Protection Act (2018). For health care providers, legal use of the electronic health record (EHR) is permitted only in clinical care cases. Any other use of the data requires thoughtful considerations of the legal context and direct patient consent. Identifiable personal and sensitive information must be sufficiently anonymized. Raw data are commonly anonymized to be used for research purposes, with risk assessment for reidentification and utility. Although health care organizations have internal policies defined for information governance, there is a significant lack of practical tools and intuitive guidance about the use of data for research and modeling. Off-the-shelf data anonymization tools are developed frequently, but privacy-related functionalities are often incomparable with regard to use in different problem domains. In addition, tools to support measuring the risk of the anonymized data with regard to reidentification against the usefulness of the data exist, but there are question marks over their efficacy. Objective In this systematic literature mapping study, we aim to alleviate the aforementioned issues by reviewing the landscape of data anonymization for digital health care. Methods We used Google Scholar, Web of Science, Elsevier Scopus, and PubMed to retrieve academic studies published in English up to June 2020. Noteworthy gray literature was also used to initialize the search. We focused on review questions covering 5 bottom-up aspects: basic anonymization operations, privacy models, reidentification risk and usability metrics, off-the-shelf anonymization tools, and the lawful basis for EHR data anonymization. Results We identified 239 eligible studies, of which 60 were chosen for general background information; 16 were selected for 7 basic anonymization operations; 104 covered 72 conventional and machine learning–based privacy models; four and 19 papers included seven and 15 metrics, respectively, for measuring the reidentification risk and degree of usability; and 36 explored 20 data anonymization software tools. In addition, we also evaluated the practical feasibility of performing anonymization on EHR data with reference to their usability in medical decision-making. Furthermore, we summarized the lawful basis for delivering guidance on practical EHR data anonymization. Conclusions This systematic literature mapping study indicates that anonymization of EHR data is theoretically achievable; yet, it requires more research efforts in practical implementations to balance privacy preservation and usability to ensure more reliable health care applications.

show abstract

Section: Resultsmentioning

confidence: 99%

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

Zuo¹,

Watson²,

Budgen³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…A widely-used tool in SMC is garbled circuit [4], a cryptographic protocol carefully designed for two-party computation, in which they can jointly evaluate a function over their sensitive data without the trust of each other. In [18], Mohammad introduced a SMC protocol for principle component analysis (PCA) which is a hybrid system utilizing additive homomorphic and garbled circuit. In secret sharing techniques [5], a secret s is distributed over multiple pieces n also called shares, where the secret can only be recovered by a sufficient amount of t shares.…”

Section: Literature Reviewmentioning

confidence: 99%

AutoGAN-based dimension reduction for privacy preservation

Nguyen

Zhuang

et al. 2020

Neurocomputing

Self Cite

View full text Add to dashboard Cite

Exploiting data and concurrently protecting sensitive information to whom data belongs is an emerging research area in data mining. Several methods have been introduced to protect individual privacy and at the same time maximize data utility. Unfortunately, existing techniques such as differential privacy are not effectively protecting data owner privacy in the scenarios using visualizable data (e.g., images, videos). Furthermore, such techniques usually result in low performance with a high number of queries. To address these problems, we propose a dimension reduction-based method for privacy preservation. This method generates dimensionally-reduced data for performing machine learning tasks and prevents a strong adversary from reconstructing the original data. In this paper, we first introduce a theoretical tool to evaluate dimension reduction-based privacy preserving mechanisms, then propose a non-linear dimension reduction framework using state-of-the-art neural network structures for privacy preservation. In the experiments, we test our method on popular face image datasets and show that our method can retain data utility and resist data reconstruction, thus protecting privacy.

show abstract

“…We carefully designed the decomposition and sort algorithms based on the MPC platform's performance characters, avoiding expensive operations and fully exploiting the opportunities to batch up operations, thus greatly reduced the computation time. In our work, we have improved performance by 200× comparing with [2] on similar scale matrices and have achieved the entire PCA on the 7, 062, 606 × 115 dataset from 9 parties within 3 minutes.…”

Section: Introductionmentioning

confidence: 99%

“…The next step is the core step of PCA, in which we perform eigen-decomposition on the covariance matrix. Previous work [2] provides an MPC-based approach to compute it on cipher-text, but the performance is unacceptable (even a 50 × 50 matrix takes 126.7 minutes). The slow performance leads to proposals to reveal the covariance matrix as plain-text for decomposition, arguing that it does not contain private information [28].…”

Section: Introductionmentioning

confidence: 99%

PPCA: Privacy-preserving Principal Component Analysis Using Secure Multiparty Computation(MPC)

Xiao-yu¹,

Chen²,

He³

et al. 2021

Preprint

View full text Add to dashboard Cite

Privacy-preserving data mining has become an important topic. People have built several multi-party-computation (MPC)-based frameworks to provide theoretically guaranteed privacy, the poor performance of real-world algorithms have always been a challenge. Using Principal Component Analysis (PCA) as an example, we show that by considering the unique performance characters of the MPC platform, we can design highly effective algorithm-level optimizations, such as replacing expensive operators and batching up. We achieve about 200× performance boost over existing privacy-preserving PCA algorithms with the same level of privacy guarantee. Also, using real-world datasets, we show that by combining multi-party data, we can achieve better training results. CCS CONCEPTS• Security and privacy → Usability in security and privacy.

show abstract

Privacy-preserving PCA on horizontally-partitioned data

Cited by 21 publications

References 12 publications

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study

AutoGAN-based dimension reduction for privacy preservation

PPCA: Privacy-preserving Principal Component Analysis Using Secure Multiparty Computation(MPC)

Contact Info

Product

Resources

About