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2012 SLDS P-20W Best Practice Conference 1 A GGREGATE R EPORTING AND D ATA D ISCLOSURE A VOIDANCE T ECHNIQUES Monday, October 29,2012 Kim Nesmith, Louisiana.

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Presentation on theme: "2012 SLDS P-20W Best Practice Conference 1 A GGREGATE R EPORTING AND D ATA D ISCLOSURE A VOIDANCE T ECHNIQUES Monday, October 29,2012 Kim Nesmith, Louisiana."— Presentation transcript:

1 2012 SLDS P-20W Best Practice Conference 1 A GGREGATE R EPORTING AND D ATA D ISCLOSURE A VOIDANCE T ECHNIQUES Monday, October 29,2012 Kim Nesmith, Louisiana Department of Education Adrian Peoples, Delaware Department of Education Baron Rodriguez, Privacy Technical Assistance Center

2 2012 SLDS P-20W Best Practice Conference Louisiana Process and Types of Suppression Delaware Public Reporting Rules and Strategy Contact Information and Resources O VERVIEW 2

3 2012 SLDS P-20W Best Practice Conference L OUISIANA 3

4 2012 SLDS P-20W Best Practice Conference Determining a “n” size Determining a percentage threshold Limiting student Level Reports and establishing MOUs F IRST S TEPS 4

5 2012 SLDS P-20W Best Practice Conference Determining what is most important Determining how to handle complementary suppression N EXT S TEPS 5

6 2012 SLDS P-20W Best Practice Conference If you can “back into a number”, the suppression is not effective o When only one number in a row or column is suppressed and the total is present o When all suppressed numbers are 0s and the total is present o If numerator, denominator, and percentage are all present C OMPLEMENTARY S UPPRESSION 6

7 2012 SLDS P-20W Best Practice Conference N S IZE E XAMPLE 7 Scholarship School Name Enrollment by Grade K123Total School A1688638 School B15000 School C30003 School D62201315110 School E31015 School F312281576 School G14 151255 TOTAL144654449302

8 2012 SLDS P-20W Best Practice Conference N S IZE E XAMPLE 8 Scholarship School Name Enrollment by Grade K123Total School A16<10 38 School B>=10<10 15 School C<10 School D62201315110 School E<10 School F>=30>=20<10>=1076 School G14 151255 TOTAL144654449302

9 2012 SLDS P-20W Best Practice Conference P ERCENT E XAMPLE 9 LEA Name All StudentsSpecial Ed. DropoutsTotal RateDropoutsTotal Rate District A612,4992.4%122654.5% District B101,2100.8%2942.1% District C455,9190.8%64571.3% District D341,1672.9%3933.2% District E73881.8%4419.8% District F234095.6%2229.1%

10 2012 SLDS P-20W Best Practice Conference P ERCENT E XAMPLE 10 LEA Name All StudentsSpecial Ed. DropoutsTotal RateDropoutsTotal Rate District A612,4992.4%122654.5% District B>10>1,210<1%<10>902.1% District C>40>5,910<1%<10>4501.3% District D341,1672.9%<10>903.2% District E<10>3801.8%<10>409.8% District F234095.6%<10>209.1%

11 2012 SLDS P-20W Best Practice Conference Talking with the requestor Creative solutions M AINTAINING T RANSPARENCY 11

12 2012 SLDS P-20W Best Practice Conference D ELAWARE 12

13 2012 SLDS P-20W Best Practice Conference Rule of X Delaware masks all data for a particular demographic if its group size is less than or equal to X 15 for Assessment, Enrollment, Teacher Quality 40 for Accountability 5/95 Rule If demographic performance is calculated to be either at or below 5% OR at or above 95%, Delaware masks the data. D ELAWARE P UBLIC R EPORTING R ULES 13

14 2012 SLDS P-20W Best Practice Conference large images 14 Database Data Application S TRATEGY : L EVEL OF I MPLEMENTATION

15 2012 SLDS P-20W Best Practice Conference 15 Data Level of Maintenance Database Application M AINTENANCE G RADIENT

16 2012 SLDS P-20W Best Practice Conference Data Suppression DO NOT SHOW data to any constituent group (e.g. public, administrators, teachers, etc.) DO NOT ALLOW aggregate data to be used as input to any data-driven decision-making Effect Suppression SHOW data to appropriate constituent group (e.g. public, administrators, teachers, etc.) DO NOT ALLOW aggregate data to be used as input to any data-driven decision-making S TRATEGY : D ATA VS. E FFECT S UPPRESSION 16

17 2012 SLDS P-20W Best Practice Conference 17 I MPLEMENTATION E XAMPLE : D ATABASE L EVEL /D ATA S UPPRESSION

18 2012 SLDS P-20W Best Practice Conference 18 I MPLEMENTATION E XAMPLE : D ATABASE L EVEL /D ATA S UPPRESSION

19 2012 SLDS P-20W Best Practice Conference 19 I MPLEMENTATION E XAMPLE : A PPLICATION L EVEL /E FFECT S UPPRESSION

20 2012 SLDS P-20W Best Practice Conference 20 B IGGEST P ITFALL : I NCONSISTENT I MPLEMENTATION Policy AccountabilityAssessmentEnrollmentTeacher Quality Small constant team Long history One point of contact Both policy and data Multiple transient contractors New contractor Bringing new reports to the public

21 2012 SLDS P-20W Best Practice Conference PTAC State-by-State analysis of public reports: 2PM today in the Burnham room. Please send a representative from your state to receive your sealed copy! Case Study 5: Minimizing Access to PII… Data De-identification: A Glossary of Terms R ESOURCES /S ESSIONS 21

22 2012 SLDS P-20W Best Practice Conference Frequently Asked Questions: 1.If I am only publishing aggregate data tables, do I still need to be concerned about disclosure avoidance? 2.What issues should educational agencies and institutions consider to successfully balance privacy protection requirements with data disclosure requirements? 3.Is public reporting of data for small groups (“small cells”) the same thing as a disclosure? 4.What standard is used to evaluate disclosure risk? 5.Does the U.S. Department of Education require educational agencies and institutions to use specific data disclosure avoidance techniques? 6.And many more… PTAC G UIDANCE FAQ’ S 22

23 2012 SLDS P-20W Best Practice Conference Contact information: Adrian Peoples, apeoples@doe.k12.de.usapeoples@doe.k12.de.us Kim Nesmith, kim.nesmith@la.govkim.nesmith@la.gov Baron Rodriguez, Baron.Rodriguez@aemcorp.comBaron.Rodriguez@aemcorp.com For more information on Aggregate Reporting: Resource 1: Presentation: Protection of Personally Identifiable Information through Disclosure Avoidance TechniquesPresentation: Protection of Personally Identifiable Information through Disclosure Avoidance Techniques Resource 2: PTAC Privacy Toolkit – Case Studies, etc.PTAC Privacy Toolkit – Case Studies, etc. Resource 3: Tech Brief #3: Statistical Methods for Protecting Personally Identifiable Information in Aggregate Reporting (DRAFT; Dec 2010)Tech Brief #3: Statistical Methods for Protecting Personally Identifiable Information in Aggregate Reporting (DRAFT; Dec 2010) C ONTACTS & A DDITIONAL R ESOURCES 23


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