Statistical and Synthetic Data Sharing with Differential Privacy
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1 pscanner and idash Data Sharing Symposium UCSD, Sept 30 Oct 2, 2015 Statistical and Synthetic Data Sharing with Differential Privacy Li Xiong Department of Mathematics and Computer Science Department of Biomedical Informatics Emory University
2 Motivation Electronic health records (EHR) Secondary use for medical research Privacy and confidentiality constraints
3 Current Practices and Limitations Raw data Patient s fear over loss of privacy and confidentiality - (un)willingness to consent Overhead of IRB reviews and policy setup De-identified data Insufficiency against re-identification and disclosure risks Lack of transparency and accountability in the use of de-identified data Lack of proof that the de-identified data is useful for PCOR studies Name Age Gender HTN Frank 42 M Y Bob 31 F Y Dave 43 M N
4 Statistical Data Sharing with Differential Privacy SHARE: Statistical and Synthetic Health Information Release with Differential Privacy (Linked R01) Building Data Registries with Privacy and Confidentiality for PCOR (PCORI) Project Team
5 SHARE: Statistical and Synthetic Health Information Release with Differential Privacy (Linked R01) Data-driven: - Multidimensionality - Correlation Application-driven: - Cross-sectional studies - Longitudinal studies Original Health Records Multi-Dimensional Data Release Sequence Data Release Statistics/ Synthetic Records SHARE: Statistical Health information RElease
6 Statistical Data Sharing with Differential Privacy
7 Statistical Data Sharing with Differential Privacy Original records Original histogram
8 Statistical Data Sharing with Differential Privacy Original records Original histogram Perturbed histogram with differential privacy
9 SHARE: Statistical and Synthetic Health Information Release with Differential Privacy (Linked R01) Data-driven: - Multidimensionality - Correlation Application-driven: - Cross-sectional studies - Longitudinal studies Original Health Records Multi-Dimensional Data Release Sequence Data Release Statistics/ Synthetic Records SHARE: Statistical Health information RElease
10 Multi-Dimensional Data Release Nonparametric methods release empirical distributions, i.e. histograms with differential privacy Parametric methods learn parameters of a distribution with differential privacy Y. Xiao, L. Xiong, L. Fan, S. Goryczka, H. Li. DPCube: Differentially Private Histogram Release through Multidimensional Partitioning, Transactions of Data Privacy (TDP), 7(3): , 2014
11 Multi-Dimensional Data Release Semi-parametric methods (DPCopula) DP marginal histograms (non-parametric) Model joint dependence using copula functions, e.g. Gaussian copula (parametric) Haoran Li, Li Xiong, Xiaoqian Jiang. Differentially Private Synthesization of Multi-Dimensional Data using Copula Functions. In EDBT 2014
12 Multi-Dimensional Data Release: Selected Results US Census data (4-dimensions) Brazil Census data (8 dimensions) DPCopula achieves lower error compared to other histogram methods Haoran Li, Li Xiong, Xiaoqian Jiang. Differentially Private Synthesization of Multi-Dimensional Data using Copula Functions. In EDBT
13 Multi-Dimensional Data Release: Addressing Data Updates Release a new histogram for each update leads to high accumulated perturbation error Distance based adaptive sampling for improved utility Can use any histogram method Haoran Li, Li Xiong, Xiaoqian Jiang, Jinfei Liu. Differentially Private Histogram Publication for Dynamic Datasets: An Adaptive Sampling Approach. CIKM 2015
14 Sequential Data Release Sequential data: longitudinal observations; encounters Prefix tree approach: sequential patterns represented as prefix tree Challenges: unsynchronized sequences; long sequences Name Blood pressure history (systolic) All: 100 Frank 110 (N) Bob 100 (N) Mary 140 (H) t1 t2 t3 120 (N) 120 (N) 130 (N) 140 (H) 140 (H) 140 (H) t1 t2 t3 L: 0 N: 70 NL: 0 NN: 40 NNL: 0 NNN: 0 NNH: 40 NH: 30 NHL: 0 NHN: 0 NHH: 30 H: 30 Original Records (Longitudinal) DP Prefix Tree James Gardner, Li Xiong, Yonghui Xiao, Jingjing Gao, Andrew Post, Xiaoqian Jiang, Lucila Ohno-Machado. SHARE: System Design and Case Studies for Statistical Health Information Release. JAMIA 2013 Luca Bonomi, Li Xiong. A Two-Phase Algorithm for Mining Sequential Patterns with Differential Privacy. CIKM 2013
15 Sequential Data Release Threshold-based pattern mining approach Generate frequent candidate k-sequences from frequent (k-1) sequences Prune infrequent ones based on database samples ID Database D Record a c d b c d a b c e d d b a d c d F 3 : freq 3-seqs Sequence {a c d} Noisy Sup. 3 C 1 : cand 1-seqs Sequence {a} {b} {c} {d} Sup {e} 1 C 3 : cand 3-seqs Sequence {a c d} Sup. 3 {a d c} 1 noise noise F 1 : freq 1-seqs Sequence {a} {c} {d} Noisy Sup F 2 : freq 2-seqs Sequence {a c} {a d} {c d} Noisy Sup {d c} 3.1 Sequence {a a} {a c} {a d} {c a} {c c} {c d} {d a} {d c} {d d} C 2 : cand 2-seqs S Xu, S Su, X Cheng, Z Li, L Xiong. Differentially Private Frequent Sequence Mining via Sampling-based Candidate Pruning. ICDE 2015 Sup noise
16 Sequential Data Release: Selected Results Frequent pattern mining achieves better utility than prefix-tree approach Threshold need to be predefined MSNBC: F-score BIBLE: F-score House_Power: F-score MSNBC: RE BIBLE: RE House_Power: RE
17 Case Studies UCSD Clinical Data Warehouse for Research (CDWR) Emory Clinical Data Warehouse (CDW) Patient encounter data extracted from Demographics Hospital encounters ICD-9 discharge diagnosis codes ICD-9 and CPT procedure codes Medication orders Laboratory test results Vital signs
18 Case Studies Study 1. Cohort discovery study Study day readmission study Compare results from the original (de-identified) data and SHARE-processed data Emory/UCSD Patient data Study Emory/UCSD Patient data SHAREprocessed Data Study
19 Building Data Registries with Privacy and Confidentiality for PCOR (PCORI) Patient empowerment Analyze and track patient risks Empower patients to make informed privacy decisions Methods to utilize consented data and respect fine-grained privacy preferences
20 Building Registries with Private and Consented Data Using private data to complement consented/public data Local and cross-center (Emory and UCSD) data registries Z Ji, X Jiang, S Wang, L Xiong, and L Ohno-Machado. Differentially Private Distributed Logistic Regression using Public and Private Biomedical Datasets. In BMC Medical Genomics 2014, 7(Suppl 1):S14 H Li, L Xiong, L Ohno-Machado, X Jiang. Privacy Preserving RBF Kernel Support Vector Machine, BioMed Research International, Volume 2014, Article , 2014
21 Data registries with fine-grained privacy control Patient privacy risks evaluation and tracking when participating in multiple data registries Building data registries with personalized privacy control Data Registry 1 Data Registry 2 Data Registry n
22 Patient Engagement and Stakeholder Panels Advisory members Patients, patient advocates, privacy officers
23 Acknowledgement Research support Center for Comprehensive Informatics Woodrow Wilson Foundation Cisco research award Students James Gardner Yonghui Xiao Collaborators Andrew Post, CCI Fusheng Wang, CCI Tyrone Grandison, IBM Chun Yuan, Tsinghua
24 Emory AIMS (Assured Information Management and Sharing) Current students Recent graduates and visitors
25 THANK YOU
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