Secure Distributed Framework for Achieving Differential Privacy

 
Secure Distributed Framework for
Achieving 
ϵ
-Differential Privacy
 
Dima Alhadidi, Noman Mohammed, Benjamin C. M. Fung, and Mourad Debbabi
Concordia Institute for Information Systems Engineering
Concordia University, Montreal, Quebec, Canada
{dm_alhad,no_moham,fung,debbabi}@encs.concordia.ca
 
Outline
 
•
Motivation
•
Problem Statement
•
Related Work
•
Background
•
Two-Party Differentially Private Data
Release
•
Performance Analysis
•
Conclusion
 
 
Outline
 
•
Motivation
•
Problem Statement
•
Related Work
•
Background
•
Two-Party Differentially Private Data
Release
•
Performance Analysis
•
Conclusion
 
Motivation
 
 
Centralized
 
Distributed
 
Motivation
 
•
Distributed: Vertically-Partitioned
 
Motivation
 
•
Distributed: Vertically-Partitioned
 
Motivation
 
•
Distributed: Horizontally-Partitioned
 
Motivation
 
•
Distributed: Horizontally-Partitioned
 
Motivation
 
•
Distributed: Horizontally-Partitioned
 
Motivation
 
•
Distributed: Horizontally-Partitioned
 
Motivation
 
•
Distributed: Horizontally-Partitioned
 
Outline
 
•
Motivation
•
Problem Statement
•
Related Work
•
Background
•
Two-Party Differentially Private Data
Release
•
Performance Analysis
•
Conclusion
 
 
Problem Statement
 
•
Desideratum to develop a two-party
data publishing algorithm for
horizontally-partitioned data which :
–
achieves differential privacy and
–
satisfies the security definition of secure
multiparty computation (SMC).
 
 
Outline
 
•
Motivation
•
Problem Statement
•
Related Work
•
Background
•
Two-Party Differentially Private Data
Release
•
Performance Analysis
•
Conclusion
 
 
Related Work
 
Outline
 
•
Motivation
•
Problem Statement
•
Related Work
•
Background
•
Two-Party Differentially Private Data
Release
•
Performance Analysis
•
Conclusion
 
 
k-Anonymity
 
k-Anonymity
Quasi-identifier (QID)
 
k-Anonymity
 
Differential Privacy
 
 
D
 
D
Laplace Mechanism
D
 
Exponential Mechanism
 
•
McSherry and Talwar have proposed
the exponential mechanism that can
choose an output  that is close to the
optimum with respect to a utility
function while preserving differential
privacy.
 
Outline
 
•
Motivation
•
Problem Statement
•
Related Work
•
Background
•
Two-Party Differentially Private Data
Release
•
Performance Analysis
•
Conclusion
 
 
Two-Party Differentially
Private Data Release
 
 
•
Generalizing the raw data
•
Adding noisy count
Generalizing the raw data
Distributed Exponential Mechanism
(DEM)
Generalization
Distributed Exponential Mechanism
(DEM)
 
Adding Noisy Count
 
•
Each party adds a Laplace noise to
its count .
•
Each party sends the result to the
other party.
 
Two-Party Protocol for
Exponential Mechanism
 
 
•
Input:
1.
Two raw data sets by two parties
2.
Set of candidates
3.
Privacy budget
•
Output : Winner candidate
 
Max Utility Function
 
D
1
 
 
Max Utility Function
 
 
D
2
 
Max Utility Function
 
D
1
 &
 
D
2
 
Computing Max Utility
Function
 
Blue-collar
 
Computing Max Utility
Function
 
max=1  
Blue-collar
 
Computing Max Utility
Function
 
max=1  
Blue-collar
Computing Max Utility
Function
max=5, sum=5  
Blue-collar
 
Computing Max Utility
Function
 
sum=5  
White-collar
 
Computing Max Utility
Function
 
max=2, sum=5 
White-collar
 
Computing Max Utility
Function
 
max=2, sum=5 
White-collar
Computing Max Utility
Function
•
max=3, sum=8 
White-collar
Result: Shares 
1 
and 
2
 
Computing the Exponential
Equation
•
Given the scores of all the
candidates, exponential mechanism
selects the candidate having score u
with the following probability:
Shares 
1 
and 
2
 
Computing the Exponential
Equation
 
 
=
Taylor Series
 
=
Computing the Exponential
Equation
 
Lowest common multiplier of {2!,…,w!}, no fraction
Approximating up to a predetermined number s
 after the decimal point
Computing the Exponential
Equation
 
No fraction
Computing the Exponential
Equation
 
Oblivious Polynomial Evaluation
First Party
Second Party
 
Result
First Party
Second Party
Computing the Exponential
Equation
 
Second
 Party
First Party
Computing the Exponential
Equation
 
0
1
 
0.5
 
0.3
 
0.2
 
0.7
Picking a random number
[0,1]
Computing the Exponential
Equation
0
Picking a random number
[0,                   ]
Picking a Random Number
 
Second
 Party
 
Random Value Protocol
[Bunn and Ostrovsky 2007]
First Party
Second
 Party
First Party
 
Picking a Winner
 
Outline
 
•
Motivation
•
Problem Statement
•
Related Work
•
Background
•
Two-Party Differentially Private Data
Release
•
Performance Analysis
•
Conclusion
 
 
Performance Analysis
 
–
Adult: 
is a 
Census data
•
6 numerical attributes.
•
8 categorical attributes.
•
45,222 census records
–
Cost Estimates
•
37.5 minutes of computation
•
37.3 minutes of communication using T
1
line with 1.544 Mbits/second bandwidth.
 
Scaling Impact
 
 
Outline
 
•
Motivation
•
Problem Statement
•
Related Work
•
Background
•
Two-Party Differentially Private Data
Release
•
Performance Analysis
•
Conclusion
 
 
Conclusion
 
•
Data release algorithm
–
Two-party
–
Differentially-private
–
Secure
–
Horizontally-partitioned
–
Non-interactive setting
 
Future Work
 
•
Consider different scenarios
–
Two parties vs. multiple parties
–
Semi-honest vs. malicious adversary
model
–
Horizontally vs. Vertically partitioned
data
•
For all these scenarios, we need
efficient algorithms

								

								
								

										

												
Slide Note

												

										

								


								
						

							
						


							

								

									
										
Embed
									
									
										
Share
									

									
									
									

								

								
							


							

							 
 							 
 							 
						  


							
							

								
This research discusses a secure distributed framework for achieving differential privacy, focusing on motivation, problem statement, related work, background, two-party differentially private data release, and performance analysis. The framework aims to address the challenges of anonymization algorithms, data distribution, and privacy preservation in centralized and distributed systems.


								
    
																		
  •   Secure Framework
    • 
																	
  •    Differential Privacy
    • 
																	
  •    Data Privacy
    • 
																	
  •    Distributed Systems
    • 
																	
  •    Anonymization Algorithms
    • 
																

								

								

									

										avts944
										

											 avts944 Follow
											

										

									

									
Uploaded on Sep 21, 2024 | 0 Views

								

								


								

									
Download Presentation

									
								

								
Please find below an Image/Link to download the presentation.

								
The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.
									Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.


									
									

									


										 
										 
										 
										 

									

									

									

									

										
 E N D 

									


								
Presentation Transcript

								

								
  1. Secure Distributed Framework for  Achieving   -Differential Privacy Dima Alhadidi, Noman Mohammed, Benjamin C. M. Fung, and Mourad Debbabi Concordia Institute for Information Systems Engineering Concordia University, Montreal, Quebec, Canada {dm_alhad,no_moham,fung,debbabi}@encs.concordia.ca
    2. 
            
            
            
  2. Outline     Motivation     Problem Statement     Related Work     Background     Two-Party Differentially Private Data  Release     Performance Analysis     Conclusion 6/24/2012 2
    3. 
            
            
            
  3. Outline     Motivation     Problem Statement     Related Work     Background     Two-Party Differentially Private Data  Release     Performance Analysis     Conclusion 6/24/2012 3
    4. 
            
            
            
  4. Motivation Centralized Individuals Data Publisher Anonymization  Algorithm Data Recipients Distributed 6/24/2012 4
    5. 
            
            
            
  5. Motivation     Distributed: Vertically-Partitioned ID ID Job Job ID Sex Salary 1 Writer 1 M 30K 2 Dancer 2 M 25K 3 Writer 3 M 35K 4 Dancer 4 F 37K 5 Engineer 5 F 65K 6 Engineer 6 F 35K 7 Engineer 7 M 30K 8 Dancer 8 F 44K 9 Lawyer 9 M 44K 10 Lawyer 10 F 44K 6/24/2012 5
    6. 
            
            
            
  6. Motivation     Distributed: Vertically-Partitioned ID ID Job Job Sex Salary 1 Writer M 30K 2 Dancer M 25K 3 Writer M 35K 4 Dancer F 37K 5 Engineer F 65K 6 Engineer F 35K 7 Engineer M 30K 8 Dancer F 44K 9 Lawyer M 44K 10 Lawyer F 44K 6/24/2012 6
    7. 
            
            
            
  7. Motivation     Distributed: Horizontally-Partitioned ID ID Job Job Sex Sex Age Age Surgery Surgery ID ID Job Job Sex Sex Age Age Surgery Surgery 8 Doctor M 58 Plastic 1 Janitor M 34 Transgender 9 Doctor M 24 Urology 2 Lawyer F 58 Plastic 10 Janitor F 63 Vascular 3 Mover M 58 Urology 11 Mover F 63 Plastic 4 Lawyer M 24 Vascular 5 Mover M 34 Transgender 6 Janitor M 44 Plastic 7 Doctor F 44 Vascular 6/24/2012 7
    8. 
            
            
            
  8. Motivation     Distributed: Horizontally-Partitioned ID ID Job Job Sex Sex Age Age Surgery Surgery 1 Janitor M 34 Transgender 2 Lawyer F 58 Plastic 3 Mover M 58 Urology 4 Lawyer M 24 Vascular 5 Mover M 34 Transgender 6 Janitor M 44 Plastic 7 Doctor F 44 Vascular 8 Doctor M 58 Plastic 9 Doctor M 24 Urology 10 Janitor F 63 Vascular 11 Mover F 63 Plastic 6/24/2012 8
    9. 
            
            
            
  9. Motivation     Distributed: Horizontally-Partitioned ID ID Job Job Sex Sex Age Age Surgery Surgery 1 Janitor M 34 Transgender 2 Lawyer F 58 Plastic 3 Mover M 58 Urology 4 Lawyer M 24 Vascular 5 Mover M 34 Transgender 6 Janitor M 44 Plastic 7 Doctor F 44 Vascular 8 Doctor M 58 Plastic 9 Doctor M 24 Urology 10 Janitor F 63 Vascular 11 Mover F 63 Plastic 6/24/2012 9
    10. 
            
            
            
  10. Motivation     Distributed: Horizontally-Partitioned ID ID Job Job Sex Sex Age Age Surgery Surgery 1 Janitor M 34 Transgender 2 Lawyer F 58 Plastic 3 Mover M 58 Urology 4 Lawyer M 24 Vascular 5 Mover M 34 Transgender 6 Janitor M 44 Plastic 7 Doctor F 44 Vascular 8 Doctor M 58 Plastic 9 Doctor M 24 Urology 10 Janitor F 63 Vascular 11 Mover F 63 Plastic 6/24/2012 10
    11. 
            
            
            
  11. Motivation     Distributed: Horizontally-Partitioned ID ID Job Job Sex Sex Age Age Surgery Surgery 1 Janitor M 34 Transgender 2 Lawyer F 58 Plastic 3 Mover M 58 Urology 4 Lawyer M 24 Vascular 5 Mover M 34 Transgender 6 Janitor M 44 Plastic 7 Doctor F 44 Vascular 8 Doctor M 58 Plastic 9 Doctor M 24 Urology 10 Janitor F 63 Vascular 11 Mover F 63 Plastic 6/24/2012 11
    12. 
            
            
            
  12. Outline     Motivation     Problem Statement     Related Work     Background     Two-Party Differentially Private Data  Release     Performance Analysis     Conclusion 6/24/2012 12
    13. 
            
            
            
  13. Problem Statement     Desideratum to develop a two-party  data publishing algorithm for  horizontally-partitioned data which :     achieves differential privacy and      satisfies the security definition of secure  multiparty computation (SMC). 6/24/2012 13
    14. 
            
            
            
  14. Outline     Motivation     Problem Statement     Related Work     Background     Two-Party Differentially Private Data  Release     Performance Analysis     Conclusion 6/24/2012 14
    15. 
            
            
            
  15. Related Work Data Owner Data Owner Privacy Model Privacy Model Algorithms Algorithms Distributed Differential  Privacy Partition- based Privacy Centralized Horizontally Vertically                 LeFevre et al., Fung et  al., etc                 Xiao et al. ,  Mohammed et al. , etc.                 Jurczyk and Xiong,  Mohammed et al.                  Jiang and Clifton,  Mohammed et al.                  Our proposal  6/24/2012 15
    16. 
            
            
            
  16. Outline     Motivation     Problem Statement     Related Work     Background     Two-Party Differentially Private Data  Release     Performance Analysis     Conclusion 6/24/2012 16
    17. 
            
            
            
  17. k-Anonymity Raw patient table Job Sex Age Disease Engineer Male 35 Fever Engineer Male 38 Fever Lawyer Male 38 Hepatitis Musician Female 30 Flu Musician Female 30 Hepatitis Dancer Female 30 Hepatitis Dancer Female 30 Hepatitis 6/24/2012 17
    18. 
            
            
            
  18. k-Anonymity Quasi-identifier (QID) Raw patient table Disease Job Sex Age Engineer Male 35 Fever Engineer Male 38 Fever Lawyer Male 38 Hepatitis Musician Female 30 Flu Musician Female 30 Hepatitis Dancer Female 30 Hepatitis Dancer Female 30 Hepatitis 6/24/2012 18
    19. 
            
            
            
  19. k-Anonymity Raw patient table 3-anonymous patient table Job Sex Age Disease Job Sex Age Disease Engineer Male 35 Fever Professional Male [36-40] Fever Engineer Male 38 Fever Professional Male [36-40] Fever Lawyer Male 38 Hepatitis Professional Male [36-40] Hepatitis Musician Female 30 Flu Artist Female [30-35] Flu Musician Female 30 Hepatitis Artist Female [30-35] Hepatitis Dancer Female 30 Hepatitis Artist Female [30-35] Hepatitis Dancer Female 30 Hepatitis Artist Female [30-35] Hepatitis 6/24/2012 19
    20. 
            
            
            
  20. Differential Privacy D    D 6/24/2012 20
    21. 
            
            
            
  21. Laplace Mechanism D 6/24/2012 21
    22. 
            
            
            
  22. Exponential Mechanism     McSherry and Talwar have proposed  the exponential mechanism that can  choose an output  that is close to the  optimum with respect to a utility  function while preserving differential  privacy. 6/24/2012 22
    23. 
            
            
            
  23. Outline     Motivation     Problem Statement     Related Work     Background     Two-Party Differentially Private Data  Release     Performance Analysis     Conclusion 6/24/2012 23
    24. 
            
            
            
  24. Two-Party Differentially  Private Data Release     Generalizing the raw data     Adding noisy count 6/24/2012 24
    25. 
            
            
            
  25. Generalizing the raw data Distributed Exponential Mechanism (DEM) 6/24/2012 25
    26. 
            
            
            
  26. Generalization Distributed Exponential Mechanism (DEM) 6/24/2012 26
    27. 
            
            
            
  27. Adding Noisy Count     Each party adds a Laplace noise to  its count .     Each party sends the result to the  other party. 6/24/2012 27
    28. 
            
            
            
  28. Two-Party Protocol for  Exponential Mechanism     Input: 1. Two raw data sets by two parties 2. Set of candidates 3. Privacy budget     Output : Winner candidate 6/24/2012 28
    29. 
            
            
            
  29. Max Utility Function ID ID Class Class Job Job Sex Sex Age Age Surgery Surgery Class Class Max Max Job Job Data Set Data Set 1 N Janitor M 34 Transgender Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 2 Y Lawyer F 58 Plastic 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar 3 Y Mover M 58 Urology D1 4 N Lawyer M 24 Vascular 5 Y Mover M 34 Transgender 3 D2 6 Y Janitor M 44 Plastic 7 Y Doctor F 44 Vascular 8 Integrated D1 and D2 D D1 1 6/24/2012 29
    30. 
            
            
            
  30. Max Utility Function ID ID Class Class Job Job Sex Sex Age Age Surgery Surgery Class Class Max Max Job Job Data Set Data Set 8 N Doctor M 58 Plastic Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 9 Y Doctor M 24 Urology 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar 10 Y Janitor F 63 Vascular D1 11 Y Mover F 63 Plastic 3 D2 D D2 2 8 Integrated D1 and D2 6/24/2012 30
    31. 
            
            
            
  31. Max Utility Function ID ID Class Class Job Job Sex Sex Age Age Surgery Surgery Class Class Max Max Job Job Data Set Data Set 1 N Janitor M 34 Transgender Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 2 Y Lawyer F 58 Plastic 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar 3 Y Mover M 58 Urology D1 4 N Lawyer M 24 Vascular 5 Y Mover M 34 Transgender 3 D2 6 Y Janitor M 44 Plastic 7 Y Doctor F 44 Vascular 8 Integrated D1 and D2 8 N Doctor M 58 Plastic 9 Y Doctor M 24 Urology 10 Y Janitor F 63 Vascular 11 Y Mover F 63 Plastic D D1 1  & &  D D2 2 6/24/2012 31
    32. 
            
            
            
  32. Computing Max Utility  Function Blue-collar Class Class Max Max Job Job Data Set Data Set Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar D1 3 D2 8 Integrated D1 and D2 6/24/2012 32
    33. 
            
            
            
  33. Computing Max Utility  Function max=1  Blue-collar Class Class Max Max Job Job Data Set Data Set Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar D1 3 D2 8 Integrated D1 and D2 6/24/2012 33
    34. 
            
            
            
  34. Computing Max Utility  Function max=1  Blue-collar Class Class Max Max Job Job Data Set Data Set Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar D1 3 D2 8 Integrated D1 and D2 6/24/2012 34
    35. 
            
            
            
  35. Computing Max Utility  Function max=5, sum=5  Blue-collar Class Class Max Max Job Job Data Set Data Set Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar D1 3 D2 8 Integrated D1 and D2 6/24/2012 35
    36. 
            
            
            
  36. Computing Max Utility  Function sum=5  White-collar Class Class Max Max Job Job Data Set Data Set Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar D1 3 D2 8 Integrated D1 and D2 6/24/2012 36
    37. 
            
            
            
  37. Computing Max Utility  Function max=2, sum=5 White-collar Class Class Max Max Job Job Data Set Data Set Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar D1 3 D2 8 Integrated D1 and D2 6/24/2012 37
    38. 
            
            
            
  38. Computing Max Utility  Function max=2, sum=5 White-collar Class Class Max Max Job Job Data Set Data Set Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar D1 3 D2 8 Integrated D1 and D2 6/24/2012 38
    39. 
            
            
            
  39. Computing Max Utility  Function     max=3, sum=8 White-collar Class Class Max Max Job Job Data Set Data Set Y Y 3 2 2 1 5 3 N N 1 1 0 1 1 2 5 Blue-collar White-collar Blue-collar White-collar Blue-collar White-collar D1 3 D2 8 Integrated D1 and D2 Result: Shares    1 and    2 6/24/2012 39
    40. 
            
            
            
  40. Computing the Exponential  Equation     Given the scores of all the  candidates, exponential mechanism  selects the candidate having score u  with the following probability: Shares    1 and    2 6/24/2012 40
    41. 
            
            
            
  41. Computing the Exponential  Equation Taylor Series = = 6/24/2012 41
    42. 
            
            
            
  42. Computing the Exponential  Equation Lowest common multiplier of {2!,   ,w!}, no fraction Approximating up to a predetermined number s  after the decimal point 6/24/2012 42
    43. 
            
            
            
  43. Computing the Exponential  Equation No fraction 6/24/2012 43
    44. 
            
            
            
  44. Computing the Exponential  Equation First Party Oblivious Polynomial Evaluation Result First Party  Second Party Second Party 6/24/2012 44
    45. 
            
            
            
  45. Computing the Exponential  Equation First Party Second Party 6/24/2012 45
    46. 
            
            
            
  46. Computing the Exponential  Equation Picking a random number [0,1] 0 1 0.2 0.7 0.3 0.5 6/24/2012 46
    47. 
            
            
            
  47. Computing the Exponential  Equation Picking a random number [0,                   ] 0 6/24/2012 47
    48. 
            
            
            
  48. Picking a Random Number First Party Second Party Random Value Protocol [Bunn and Ostrovsky 2007] Second Party First Party 6/24/2012 48
    49. 
            
            
            
  49. Picking a Winner 6/24/2012 49
    50. 
            
            
            
  50. Outline     Motivation     Problem Statement     Related Work     Background     Two-Party Differentially Private Data  Release     Performance Analysis     Conclusion 6/24/2012 50
    51. 
            
            
            
							

							

					

					
					
					

						
Related

						

						


							
							
							
							

						

							

									

      

        
          

            

              Dynamic Pricing Algorithms with Privacy Preservation in Personalized Decision Making
            

            

              
Dynamic Pricing Algorithms with Privacy Preservation in Personalized Decision Making

              
lillia

            

          

        
      


     

      

        
          

            

              Overview of Distributed Systems: Characteristics, Classification, Computation, Communication, and Fault Models
            

            

              
Overview of Distributed Systems: Characteristics, Classification, Computation, Communication, and Fault Models

              
paulina

            

          

        
      


     

      

        
          

            

              WBC Differential Count in Blood Analysis
            

            

              
WBC Differential Count in Blood Analysis

              
lillymae

            

          

        
      


     

      

        
          

            

              Privacy in Information Security Training
            

            

              
Privacy in Information Security Training

              
lillia

            

          

        
      


     

      

        
          

            

              NCVHS Subcommittee on Privacy Comments on HIPAA Privacy Rule for Reproductive Health Care
            

            

              
NCVHS Subcommittee on Privacy Comments on HIPAA Privacy Rule for Reproductive Health Care

              
haley

            

          

        
      


     

      

        
          

            

              Privacy-Preserving Analysis of Graph Structures
            

            

              
Privacy-Preserving Analysis of Graph Structures

              
bilal

            

          

        
      


     

      

        
          

            

              Differential Equations: Types, Classification, and Solutions
            

            

              
Differential Equations: Types, Classification, and Solutions

              
allie

            

          

        
      


     

      

        
          

            

              Differential Equations in Economics Honours
            

            

              
Differential Equations in Economics Honours

              
damaris

            

          

        
      


     

      

        
          

            

              Introduction to Differential Equations and Laplace Equations
            

            

              
Introduction to Differential Equations and Laplace Equations

              
una

            

          

        
      


     

      

        
          

            

              Privacy-Preserving Prediction and Learning in Machine Learning Research
            

            

              
Privacy-Preserving Prediction and Learning in Machine Learning Research

              
eviegrace

            

          

        
      


     

      

        
          

            

              FMCSA Privacy Awareness Training Overview for 2022
            

            

              
FMCSA Privacy Awareness Training Overview for 2022

              
natan

            

          

        
      


     

      

        
          

            

              Data Privacy Laws and Regulations in Saudi Arabia
            

            

              
Data Privacy Laws and Regulations in Saudi Arabia

              
twyla

            

          

        
      


     

      

        
          

            

              Blockchain Data Structures and Hyperledger Implementation
            

            

              
Blockchain Data Structures and Hyperledger Implementation

              
harold

            

          

        
      


     

      

        
          

            

              Privacy Considerations in Data Management for Data Science Lecture
            

            

              
Privacy Considerations in Data Management for Data Science Lecture

              
maen728

            

          

        
      


     

      

        
          

            

              Secure Multiparty Computation: Enhancing Privacy in Data Sharing
            

            

              
Secure Multiparty Computation: Enhancing Privacy in Data Sharing

              
mino_ll

            

          

        
      


     

      

        
          

            

              CMRR in Differential Amplifiers
            

            

              
CMRR in Differential Amplifiers

              
keymons

            

          

        
      


     

      

        
          

            

              The Privacy Paradox: Attitudes vs. Behaviors
            

            

              
The Privacy Paradox: Attitudes vs. Behaviors

              
clai_evi

            

          

        
      


     

      

        
          

            

              Applications of Differential Equations in Engineering and Mathematics
            

            

              
Applications of Differential Equations in Engineering and Mathematics

              
wil_kin

            

          

        
      


     

      

        
          

            

              Distributed Hash Table (DHT) in Distributed Systems
            

            

              
Distributed Hash Table (DHT) in Distributed Systems

              
zerahw

            

          

        
      


     

      

        
          

            

              Privacy Rights in Europe: A Comprehensive Overview
            

            

              
Privacy Rights in Europe: A Comprehensive Overview

              
jesu_26

            

          

        
      


     

      

        
          

            

              Distributed Computing Systems Project: Distributed Shell Implementation
            

            

              
Distributed Computing Systems Project: Distributed Shell Implementation

              
nian164

            

          

        
      


     

      

        
          

            

              Data Privacy Best Practices Training for Libraries
            

            

              
Data Privacy Best Practices Training for Libraries

              
teo_nah

            

          

        
      


     							

						

					

					

				


				

			

		
		

			

				
				

					
More Related Content

					

						

														

					

				

				
			

		












 





  
  
  



giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#