SOCR News UMich SPH MLEED 2019 - Revision history

Dinov: /* Presentation */

2019-11-27T21:53:51Z

‎Presentation

Dinov: /* Logistics */

2019-11-27T21:04:00Z

‎Logistics

Dinov: /* Logistics */

2019-11-27T16:24:05Z

‎Logistics

Dinov: /* References */

2019-11-25T16:10:52Z

‎References

Dinov: /* Logistics */

2019-11-25T16:09:40Z

‎Logistics

Dinov: /* References */

2019-10-01T17:49:08Z

‎References

Dinov: Created page with "== SOCR News & Events: SOCR DataSifter: A Statistical Obfuscation Technique enabling Effective Data Sharing == ==Logistics== Image:SOCR_Hat_2019.png|150px..."

2019-10-01T17:45:52Z

Created page with "== SOCR News & Events: SOCR DataSifter: A Statistical Obfuscation Technique enabling Effective Data Sharing == ==Logistics== Image:SOCR_Hat_2019.png|150px..."

New page

== [[SOCR_News | SOCR News & Events]]: SOCR DataSifter: A Statistical Obfuscation Technique enabling Effective Data Sharing ==

==Logistics==
[[Image:SOCR_Hat_2019.png|150px|thumbnail|right| [http://socr.umich.edu SOCR Resource] ]]
* '''Seminar Series''': [http://mleead.umich.edu/Events.php University of Michigan SPH Environmental Epidemiology Seminar Series]
* '''Date/Times''': Tuesday, Dec. 3, 2019, 12 PM (Noon).
* '''Place/Time''': TBD

==Presentation==
* '''Presenter''': [http://umich.edu/~dinov Ivo D. Dinov], joint work with Nina Zhou, Simeone Marino, Yi Zhao, Lu Wei, Lu Wang.
* '''Title''': ''SOCR DataSifter: A Statistical Obfuscation Technique enabling Effective Data Sharing''
* '''Abstract''': Effective and pragmatic sharing of data that includes sensitive information is difficult. The validation and reproducibility of findings in many health, financial, intelligence, socioeconomic, and other high-dimensional case-studies is inhibited when the data can’t be shared and the results independently confirmed. Either the utility of the data may be compromised by significant masking of the data or alternatively there may be a high risk of exposing private personal or secure organizational information. Excessive scrambling or encoding of the information makes the information less useful for modeling, or analytical processing. Insufficient preprocessing may compromise sensitive information and introduce a substantial risk for re-identification of individuals by various stratification techniques.

:

: To address this problem, the SOCR lab developed [http://DataSifter.org a novel statistical method (DataSifter)] that provides on-the-fly obfuscation of high-dimensional structured and unstructured sensitive data, e.g., clinical data from electronic health records (EHR). This technique provides complete administrative control over the balance between risk of data re-identification and preservation of the data information. Under careful set up of user-defined privacy levels, our simulation experiments suggest that the DataSifter protects privacy while maintaining data utility for different types of outcomes of interest. The application of DataSifter on ABIDE data provides a realistic demonstration of how to employ the proposed algorithm on EHR with more than 500 features. We are extending the DataSifter to desensitize longitudinal data and free-text. Time-permitting, some additional SOCR tools and resources may be demonstrated (http://www.socr.umich.edu).

==Demos==
* [http://socr.umich.edu/HTML5/ General SOCR Webapps]
* [http://socr.umich.edu/HTML5/BrainViewer/ SOCR BrainViewer]
* [http://socr.umich.edu/HTML5/SOCR_TensorBoard_UKBB/ Hands-on interactive visualization of extremely high-dimensional data (learning module and webapp)]
** [http://socr.umich.edu/HTML5/SOCR_TensorBoard_UKBB/UsersData.html Country Ranking study]
** [http://socr.umich.edu/CSCD/html/Cores/Macore2/CSCD_SOCR_Diabetes_CaseStudy/ Diabetes Case-Study]

<hr>

==References==
* SOCR is sponsored in part by NIH Grants [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 P30 DK089503], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8821268&icde=22205726 P20 NR015331], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8907508&icde=22205754 U54 EB020406], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8882615&icde=22023333 P50 NS091856], [https://projectreporter.nih.gov/project_info_description.cfm?aid=9172096&icde=30598205 P30AG053760], as well as, NSF Grants [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1734853 1734853] and [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1636840 1636840].
* DataSifter: http://DataSifter.org
* SOCR Home page: http://www.socr.umich.edu

{{translate|pageName=http://wiki.socr.umich.edu/index.php/SOCR_News_UMich_SPH_MLEED_2019}}

@@ Line 4: / Line 4: @@
 ==Logistics==
 [[Image:SOCR_BeanieHat_2020.png|150px|thumbnail|right| [http://socr.umich.edu SOCR Resource] ]]
-* '''Seminar Series''': [http://mleead.umich.edu/Events.php University of Michigan SPH Environmental Epidemiology Seminar Series]
+* '''Seminar Series''': [https://sph.umich.edu/events/event.php?ID=8233 University of Michigan SPH Environmental Epidemiology Seminar Series]
 * '''Date/Times''': Tuesday, Dec. 3, 2019, 12 PM (Noon).
 * '''Place/Time''': [https://maps.studentlife.umich.edu/building/henry-frieze-vaughan-public-health-building 3755 SPH I] (School of Public Health, 1415 Washington Heights)

@@ Line 15: / Line 15: @@
+:
 : To address this problem, the SOCR lab developed [http://DataSifter.org a novel statistical method (DataSifter)] that provides on-the-fly obfuscation of high-dimensional structured and unstructured sensitive data, e.g., clinical data from electronic health records (EHR). This technique provides complete administrative control over the balance between risk of data re-identification and preservation of the data information. Under careful set up of user-defined privacy levels, our simulation experiments suggest that the DataSifter protects privacy while maintaining data utility for different types of outcomes of interest. The application of DataSifter on ABIDE data provides a realistic demonstration of how to employ the proposed algorithm on EHR with more than 500 features. We are extending the DataSifter to desensitize longitudinal data and free-text. Time-permitting, some additional SOCR tools and resources may be demonstrated (http://www.socr.umich.edu).
 ==Demos==

@@ Line 31: / Line 31: @@
 * SOCR is sponsored in part by NIH Grants [http://projectreporter.nih.gov/project_info_details.cfm?aid=8975330&icde=25689118 P30 DK089503], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8821268&icde=22205726 P20 NR015331], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8907508&icde=22205754 U54 EB020406], [http://projectreporter.nih.gov/project_info_description.cfm?aid=8882615&icde=22023333 P50 NS091856], [https://projectreporter.nih.gov/project_info_description.cfm?aid=9172096&icde=30598205 P30AG053760], as well as, NSF Grants [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1734853 1734853] and [http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1636840 1636840].
 * DataSifter: http://DataSifter.org
 * [http://www.freepatentsonline.com/y2019/0042791.html US Patent US20190042791]
 * SOCR Home page: http://www.socr.umich.edu