SlideShare a Scribd company logo

Theory of Data Entanglement Protects Remotely Stored Data

•Download as PPT, PDF•
•
Aleksandr Yampolskiy
Aleksandr Yampolskiy
1 of 21
Towards a Theory of Data Entanglement James Aspnes, Joan Feigenbaum, Aleksandr Yampolskiy, and Sheng Zhong (Yale University)
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Goal: Protect Remotely Stored Data from the Server ,[object Object],[object Object]
Previous Work: Dagster [SW01] New Document  Encrypt c randomly chosen blocks Pool of blocks Analysis: Deleting a typical document  loss of O( c ) documents
Previous Work: Tangler [WM01] (0, New Document) 2 randomly chosen blocks Pool of n blocks Analysis: Deleting a typical document  loss of O ( (log n ) / n ) documents Interpolate degree-2 poly F() (x 1 ,F(x 1 )) (x 2 ,F(x 2 ))
Our Model: Basic Framework ,[object Object],[object Object],[object Object],encoding E … d 1 d 2 d n initializer I k 1 k 2 k n k E tamperer storage server
Our Model: Basic Framework (cont.) ,[object Object],[object Object],… k 1 k 2 k n storage server
Our Model : Classification ,[object Object],[object Object],[object Object],[object Object],[object Object]
Our Model : Classification (cont.) ,[object Object],[object Object],[object Object],[object Object],… … …
Our Model : Classification (cont.) ,[object Object],[object Object],[object Object],[object Object]
Our Definitions ,[object Object],[object Object]
Our Definitions (cont.) ,[object Object],d 1 d 2 d 3 d 4 d 1 depends on d 2
Our Definitions (cont.) ,[object Object],d 1 d 2 d 3 d 4
Our Definitions (cont.) ,[object Object]
Possibility of AONI in Standard-Recovery Model ,[object Object],[object Object],[object Object],[object Object],[object Object]
Impossibility of AONI in Public and Private-Recovery Models ,[object Object],[object Object],[object Object]
Impossibility of AONI in Public and Private-Recovery Models (cont.) ,[object Object],[object Object],…
Possibility of Symmetric Recovery in Public-Recovery Model ,[object Object],[object Object],[object Object],[object Object]
Possibility of AONI for Destructive Adversaries ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary of Results  all-or-nothing Private Recovery symmetric recovery all-or-nothing Public Recovery all-or-nothing all-or-nothing Standard Recovery Arbitrary Tamperer Destructive Tamperer
Future Work ,[object Object],[object Object]

Recommended

JSpiders - Wrapper classes
JSpiders - Wrapper classesJSpiders - Wrapper classes
JSpiders - Wrapper classesJSpiders Basavanagudi
 
Algo>Abstract data type
Algo>Abstract data typeAlgo>Abstract data type
Algo>Abstract data typeAin-ul-Moiz Khawaja
 
ClassesandObjects
ClassesandObjectsClassesandObjects
ClassesandObjectsYourVirtual Class
 
Session 06 machine learning.pptx
Session 06 machine learning.pptxSession 06 machine learning.pptx
Session 06 machine learning.pptxbodaceacat
 
Data types, Variables, Expressions & Arithmetic Operators in java
Data types, Variables, Expressions & Arithmetic Operators in javaData types, Variables, Expressions & Arithmetic Operators in java
Data types, Variables, Expressions & Arithmetic Operators in javaJaved Rashid
 
Session 07 text data.pptx
Session 07 text data.pptxSession 07 text data.pptx
Session 07 text data.pptxbodaceacat
 
Jan vitek distributedrandomforest_5-2-2013
Jan vitek distributedrandomforest_5-2-2013Jan vitek distributedrandomforest_5-2-2013
Jan vitek distributedrandomforest_5-2-2013Sri Ambati
 
Chapter 7 - Defining Your Own Classes - Part II
Chapter 7 - Defining Your Own Classes - Part IIChapter 7 - Defining Your Own Classes - Part II
Chapter 7 - Defining Your Own Classes - Part IIEduardo Bergavera
 

Recommended

JSpiders - Wrapper classes
JSpiders - Wrapper classesJSpiders - Wrapper classes
JSpiders - Wrapper classesJSpiders Basavanagudi
 
Algo>Abstract data type
Algo>Abstract data typeAlgo>Abstract data type
Algo>Abstract data typeAin-ul-Moiz Khawaja
 
ClassesandObjects
ClassesandObjectsClassesandObjects
ClassesandObjectsYourVirtual Class
 
Session 06 machine learning.pptx
Session 06 machine learning.pptxSession 06 machine learning.pptx
Session 06 machine learning.pptxbodaceacat
 
Data types, Variables, Expressions & Arithmetic Operators in java
Data types, Variables, Expressions & Arithmetic Operators in javaData types, Variables, Expressions & Arithmetic Operators in java
Data types, Variables, Expressions & Arithmetic Operators in javaJaved Rashid
 
Session 07 text data.pptx
Session 07 text data.pptxSession 07 text data.pptx
Session 07 text data.pptxbodaceacat
 
Jan vitek distributedrandomforest_5-2-2013
Jan vitek distributedrandomforest_5-2-2013Jan vitek distributedrandomforest_5-2-2013
Jan vitek distributedrandomforest_5-2-2013Sri Ambati
 
Chapter 7 - Defining Your Own Classes - Part II
Chapter 7 - Defining Your Own Classes - Part IIChapter 7 - Defining Your Own Classes - Part II
Chapter 7 - Defining Your Own Classes - Part IIEduardo Bergavera
 
Icom4015 lecture3-f16
Icom4015 lecture3-f16Icom4015 lecture3-f16
Icom4015 lecture3-f16BienvenidoVelezUPR
 
Java Programming
Java ProgrammingJava Programming
Java ProgrammingNanthini Kempaiyan
 
Introduction on Data Structures
Introduction on Data StructuresIntroduction on Data Structures
Introduction on Data StructuresNanthini Kempaiyan
 
Introduction java programming
Introduction java programmingIntroduction java programming
Introduction java programmingNanthini Kempaiyan
 
Icom4015 lecture7-f16
Icom4015 lecture7-f16Icom4015 lecture7-f16
Icom4015 lecture7-f16BienvenidoVelezUPR
 
Chapter 13 introduction to classes
Chapter 13 introduction to classesChapter 13 introduction to classes
Chapter 13 introduction to classesrsnyder3601
 
1207028 634528828886611250
1207028 6345288288866112501207028 634528828886611250
1207028 634528828886611250Akhil Nama
 
Data structures using C
Data structures using CData structures using C
Data structures using CPdr Patnaik
 
Abstract data types
Abstract data typesAbstract data types
Abstract data typesHoang Nguyen
 
To allot secrecy-safe association rules mining schema using FP tree
To allot secrecy-safe association rules mining schema using FP treeTo allot secrecy-safe association rules mining schema using FP tree
To allot secrecy-safe association rules mining schema using FP treeUvaraj Shan
 
Advanced Programming Lecture 6 Fall 2016
Advanced Programming Lecture 6 Fall 2016Advanced Programming Lecture 6 Fall 2016
Advanced Programming Lecture 6 Fall 2016BienvenidoVelezUPR
 
02 data types in java
02 data types in java02 data types in java
02 data types in javaরাকিন রাকিন
 
Week 4 dq 4
Week 4 dq 4Week 4 dq 4
Week 4 dq 4amaxwell2012
 
Java basics variables
Java basics variables Java basics variables
Java basics variablesJoeReddieMedia
 
Training deep auto encoders for collaborative filtering
Training deep auto encoders for collaborative filteringTraining deep auto encoders for collaborative filtering
Training deep auto encoders for collaborative filteringMarlesson Santana
 
Unit 1 abstract data types
Unit 1 abstract data typesUnit 1 abstract data types
Unit 1 abstract data typesLavanyaJ28
 
Causes of dropping_out
Causes of dropping_outCauses of dropping_out
Causes of dropping_outisaflo
 
Search Engine Marketing
Search Engine MarketingSearch Engine Marketing
Search Engine MarketingChristina Hawkins
 
Urbanization
UrbanizationUrbanization
Urbanizationncoggan
 
Spotify vs Radio Ads
Spotify vs Radio AdsSpotify vs Radio Ads
Spotify vs Radio AdsRealOwls
 
Threshold and Proactive Pseudo-Random Permutations
Threshold and Proactive Pseudo-Random PermutationsThreshold and Proactive Pseudo-Random Permutations
Threshold and Proactive Pseudo-Random PermutationsAleksandr Yampolskiy
 
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing ItYou Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing ItAleksandr Yampolskiy
 

More Related Content

What's hot

Introduction on Data Structures
Introduction on Data StructuresIntroduction on Data Structures
Introduction on Data StructuresNanthini Kempaiyan
 
Chapter 13 introduction to classes
Chapter 13 introduction to classesChapter 13 introduction to classes
Chapter 13 introduction to classesrsnyder3601
 
1207028 634528828886611250
1207028 6345288288866112501207028 634528828886611250
1207028 634528828886611250Akhil Nama
 
Data structures using C
Data structures using CData structures using C
Data structures using CPdr Patnaik
 
Abstract data types
Abstract data typesAbstract data types
Abstract data typesHoang Nguyen
 
To allot secrecy-safe association rules mining schema using FP tree
To allot secrecy-safe association rules mining schema using FP treeTo allot secrecy-safe association rules mining schema using FP tree
To allot secrecy-safe association rules mining schema using FP treeUvaraj Shan
 
Advanced Programming Lecture 6 Fall 2016
Advanced Programming Lecture 6 Fall 2016Advanced Programming Lecture 6 Fall 2016
Advanced Programming Lecture 6 Fall 2016BienvenidoVelezUPR
 
Java basics variables
Java basics variables Java basics variables
Java basics variablesJoeReddieMedia
 
Training deep auto encoders for collaborative filtering
Training deep auto encoders for collaborative filteringTraining deep auto encoders for collaborative filtering
Training deep auto encoders for collaborative filteringMarlesson Santana
 
Unit 1 abstract data types
Unit 1 abstract data typesUnit 1 abstract data types
Unit 1 abstract data typesLavanyaJ28
 

What's hot (16)

Icom4015 lecture3-f16
Icom4015 lecture3-f16Icom4015 lecture3-f16
Icom4015 lecture3-f16
 
Java Programming
Java ProgrammingJava Programming
Java Programming
 
Introduction on Data Structures
Introduction on Data StructuresIntroduction on Data Structures
Introduction on Data Structures
 
Introduction java programming
Introduction java programmingIntroduction java programming
Introduction java programming
 
Icom4015 lecture7-f16
Icom4015 lecture7-f16Icom4015 lecture7-f16
Icom4015 lecture7-f16
 
Chapter 13 introduction to classes
Chapter 13 introduction to classesChapter 13 introduction to classes
Chapter 13 introduction to classes
 
1207028 634528828886611250
1207028 6345288288866112501207028 634528828886611250
1207028 634528828886611250
 
Data structures using C
Data structures using CData structures using C
Data structures using C
 
Abstract data types
Abstract data typesAbstract data types
Abstract data types
 
To allot secrecy-safe association rules mining schema using FP tree
To allot secrecy-safe association rules mining schema using FP treeTo allot secrecy-safe association rules mining schema using FP tree
To allot secrecy-safe association rules mining schema using FP tree
 
Advanced Programming Lecture 6 Fall 2016
Advanced Programming Lecture 6 Fall 2016Advanced Programming Lecture 6 Fall 2016
Advanced Programming Lecture 6 Fall 2016
 
02 data types in java
02 data types in java02 data types in java
02 data types in java
 
Week 4 dq 4
Week 4 dq 4Week 4 dq 4
Week 4 dq 4
 
Java basics variables
Java basics variables Java basics variables
Java basics variables
 
Training deep auto encoders for collaborative filtering
Training deep auto encoders for collaborative filteringTraining deep auto encoders for collaborative filtering
Training deep auto encoders for collaborative filtering
 
Unit 1 abstract data types
Unit 1 abstract data typesUnit 1 abstract data types
Unit 1 abstract data types
 

Viewers also liked

Causes of dropping_out
Causes of dropping_outCauses of dropping_out
Causes of dropping_outisaflo
 
Urbanization
UrbanizationUrbanization
Urbanizationncoggan
 
Spotify vs Radio Ads
Spotify vs Radio AdsSpotify vs Radio Ads
Spotify vs Radio AdsRealOwls
 
Threshold and Proactive Pseudo-Random Permutations
Threshold and Proactive Pseudo-Random PermutationsThreshold and Proactive Pseudo-Random Permutations
Threshold and Proactive Pseudo-Random PermutationsAleksandr Yampolskiy
 
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing ItYou Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing ItAleksandr Yampolskiy
 
Class powerpoint
Class powerpointClass powerpoint
Class powerpointncoggan
 
New York REDIS Meetup Welcome Session
New York REDIS Meetup Welcome SessionNew York REDIS Meetup Welcome Session
New York REDIS Meetup Welcome SessionAleksandr Yampolskiy
 

Viewers also liked (8)

Causes of dropping_out
Causes of dropping_outCauses of dropping_out
Causes of dropping_out
 
Search Engine Marketing
Search Engine MarketingSearch Engine Marketing
Search Engine Marketing
 
Urbanization
UrbanizationUrbanization
Urbanization
 
Spotify vs Radio Ads
Spotify vs Radio AdsSpotify vs Radio Ads
Spotify vs Radio Ads
 
Threshold and Proactive Pseudo-Random Permutations
Threshold and Proactive Pseudo-Random PermutationsThreshold and Proactive Pseudo-Random Permutations
Threshold and Proactive Pseudo-Random Permutations
 
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing ItYou Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
You Too Can Be a Radio Host Or How We Scaled a .NET Startup And Had Fun Doing It
 
Class powerpoint
Class powerpointClass powerpoint
Class powerpoint
 
New York REDIS Meetup Welcome Session
New York REDIS Meetup Welcome SessionNew York REDIS Meetup Welcome Session
New York REDIS Meetup Welcome Session
 

Similar to Theory of Data Entanglement Protects Remotely Stored Data

Sheng defense
Sheng defenseSheng defense
Sheng defensekalyan_bu
 
PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVO...
PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVO... PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVO...
PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVO...Nexgen Technology
 
Towards explanations for Data-Centric AI using provenance records
Towards explanations for Data-Centric AI using provenance recordsTowards explanations for Data-Centric AI using provenance records
Towards explanations for Data-Centric AI using provenance recordsPaolo Missier
 
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTINGFAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTINGIJNSA Journal
 
Methodological study of opinion mining and sentiment analysis techniques
Methodological study of opinion mining and sentiment analysis techniquesMethodological study of opinion mining and sentiment analysis techniques
Methodological study of opinion mining and sentiment analysis techniquesijsc
 
Black-Box attacks against Neural Networks - technical project report
Black-Box attacks against Neural Networks - technical project reportBlack-Box attacks against Neural Networks - technical project report
Black-Box attacks against Neural Networks - technical project reportRoberto Falconi
 
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTINGFAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTINGIJNSA Journal
 
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...Sunny Kr
 
DagdelenSiriwardaneY..
DagdelenSiriwardaneY..DagdelenSiriwardaneY..
DagdelenSiriwardaneY..butest
 
Machine_Learning_Trushita
Machine_Learning_TrushitaMachine_Learning_Trushita
Machine_Learning_TrushitaTrushita Redij
 
Privacy-preserving Information Sharing: Tools and Applications
Privacy-preserving Information Sharing: Tools and ApplicationsPrivacy-preserving Information Sharing: Tools and Applications
Privacy-preserving Information Sharing: Tools and ApplicationsEmiliano De Cristofaro
 
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESIMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESVikash Kumar
 
1st review android malware.pptx
1st review android malware.pptx1st review android malware.pptx
1st review android malware.pptxNambiraju
 
House price prediction
House price predictionHouse price prediction
House price predictionSabahBegum
 
Black-Box attacks against Neural Networks - technical project presentation
Black-Box attacks against Neural Networks - technical project presentationBlack-Box attacks against Neural Networks - technical project presentation
Black-Box attacks against Neural Networks - technical project presentationRoberto Falconi
 
Presentation_Malware Analysis.pptx
Presentation_Malware Analysis.pptxPresentation_Malware Analysis.pptx
Presentation_Malware Analysis.pptxnishanth kurush
 
Machine Learning for Incident Detection: Getting Started
Machine Learning for Incident Detection: Getting StartedMachine Learning for Incident Detection: Getting Started
Machine Learning for Incident Detection: Getting StartedSqrrl
 
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques Methodological Study Of Opinion Mining And Sentiment Analysis Techniques
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques ijsc
 
Automatic DNA Sequence Generation for Secured Effective Multi -Cloud Storage
Automatic DNA Sequence Generation for Secured Effective Multi -Cloud StorageAutomatic DNA Sequence Generation for Secured Effective Multi -Cloud Storage
Automatic DNA Sequence Generation for Secured Effective Multi -Cloud StorageIOSR Journals
 

Similar to Theory of Data Entanglement Protects Remotely Stored Data (20)

Sheng defense
Sheng defenseSheng defense
Sheng defense
 
PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVO...
PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVO... PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVO...
PUBLIC INTEGRITY AUDITING FOR SHARED DYNAMIC CLOUD DATA WITH GROUP USER REVO...
 
Towards explanations for Data-Centric AI using provenance records
Towards explanations for Data-Centric AI using provenance recordsTowards explanations for Data-Centric AI using provenance records
Towards explanations for Data-Centric AI using provenance records
 
[IJET V2I3P11] Authors: Payal More, Rohini Pandit, Supriya Makude, Harsh Nirb...
[IJET V2I3P11] Authors: Payal More, Rohini Pandit, Supriya Makude, Harsh Nirb...[IJET V2I3P11] Authors: Payal More, Rohini Pandit, Supriya Makude, Harsh Nirb...
[IJET V2I3P11] Authors: Payal More, Rohini Pandit, Supriya Makude, Harsh Nirb...
 
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTINGFAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
 
Methodological study of opinion mining and sentiment analysis techniques
Methodological study of opinion mining and sentiment analysis techniquesMethodological study of opinion mining and sentiment analysis techniques
Methodological study of opinion mining and sentiment analysis techniques
 
Black-Box attacks against Neural Networks - technical project report
Black-Box attacks against Neural Networks - technical project reportBlack-Box attacks against Neural Networks - technical project report
Black-Box attacks against Neural Networks - technical project report
 
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTINGFAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
FAST DETECTION OF DDOS ATTACKS USING NON-ADAPTIVE GROUP TESTING
 
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardin...
 
DagdelenSiriwardaneY..
DagdelenSiriwardaneY..DagdelenSiriwardaneY..
DagdelenSiriwardaneY..
 
Machine_Learning_Trushita
Machine_Learning_TrushitaMachine_Learning_Trushita
Machine_Learning_Trushita
 
Privacy-preserving Information Sharing: Tools and Applications
Privacy-preserving Information Sharing: Tools and ApplicationsPrivacy-preserving Information Sharing: Tools and Applications
Privacy-preserving Information Sharing: Tools and Applications
 
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESIMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHES
 
1st review android malware.pptx
1st review android malware.pptx1st review android malware.pptx
1st review android malware.pptx
 
House price prediction
House price predictionHouse price prediction
House price prediction
 
Black-Box attacks against Neural Networks - technical project presentation
Black-Box attacks against Neural Networks - technical project presentationBlack-Box attacks against Neural Networks - technical project presentation
Black-Box attacks against Neural Networks - technical project presentation
 
Presentation_Malware Analysis.pptx
Presentation_Malware Analysis.pptxPresentation_Malware Analysis.pptx
Presentation_Malware Analysis.pptx
 
Machine Learning for Incident Detection: Getting Started
Machine Learning for Incident Detection: Getting StartedMachine Learning for Incident Detection: Getting Started
Machine Learning for Incident Detection: Getting Started
 
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques Methodological Study Of Opinion Mining And Sentiment Analysis Techniques
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques
 
Automatic DNA Sequence Generation for Secured Effective Multi -Cloud Storage
Automatic DNA Sequence Generation for Secured Effective Multi -Cloud StorageAutomatic DNA Sequence Generation for Secured Effective Multi -Cloud Storage
Automatic DNA Sequence Generation for Secured Effective Multi -Cloud Storage
 

More from Aleksandr Yampolskiy

"Managing software development" by Peter Bell
"Managing software development" by Peter Bell"Managing software development" by Peter Bell
"Managing software development" by Peter BellAleksandr Yampolskiy
 
Recruiting Great Engineers in Six Easy Steps
Recruiting Great Engineers in Six Easy StepsRecruiting Great Engineers in Six Easy Steps
Recruiting Great Engineers in Six Easy StepsAleksandr Yampolskiy
 
Malware Goes to the Movies - Briefing
Malware Goes to the Movies - BriefingMalware Goes to the Movies - Briefing
Malware Goes to the Movies - BriefingAleksandr Yampolskiy
 
Eight simple rules to writing secure PHP programs
Eight simple rules to writing secure PHP programsEight simple rules to writing secure PHP programs
Eight simple rules to writing secure PHP programsAleksandr Yampolskiy
 
Social Engineering and What to do About it
Social Engineering and What to do About itSocial Engineering and What to do About it
Social Engineering and What to do About itAleksandr Yampolskiy
 
Inoculation strategies for victims of viruses
Inoculation strategies for victims of virusesInoculation strategies for victims of viruses
Inoculation strategies for victims of virusesAleksandr Yampolskiy
 
Much ado about randomness. What is really a random number?
Much ado about randomness. What is really a random number?Much ado about randomness. What is really a random number?
Much ado about randomness. What is really a random number?Aleksandr Yampolskiy
 
Secure information aggregation in sensor networks
Secure information aggregation in sensor networksSecure information aggregation in sensor networks
Secure information aggregation in sensor networksAleksandr Yampolskiy
 
A verifiable random function with short proofs and keys
A verifiable random function with short proofs and keysA verifiable random function with short proofs and keys
A verifiable random function with short proofs and keysAleksandr Yampolskiy
 
Price of anarchy is independent of network topology
Price of anarchy is independent of network topologyPrice of anarchy is independent of network topology
Price of anarchy is independent of network topologyAleksandr Yampolskiy
 
Spreading Rumors Quietly and the Subgroup Escape Problem
Spreading Rumors Quietly and the Subgroup Escape ProblemSpreading Rumors Quietly and the Subgroup Escape Problem
Spreading Rumors Quietly and the Subgroup Escape ProblemAleksandr Yampolskiy
 

More from Aleksandr Yampolskiy (18)

"Managing software development" by Peter Bell
"Managing software development" by Peter Bell"Managing software development" by Peter Bell
"Managing software development" by Peter Bell
 
Recruiting Great Engineers in Six Easy Steps
Recruiting Great Engineers in Six Easy StepsRecruiting Great Engineers in Six Easy Steps
Recruiting Great Engineers in Six Easy Steps
 
Malware Goes to the Movies - Briefing
Malware Goes to the Movies - BriefingMalware Goes to the Movies - Briefing
Malware Goes to the Movies - Briefing
 
Privacy and E-Commerce
Privacy and E-CommercePrivacy and E-Commerce
Privacy and E-Commerce
 
Eight simple rules to writing secure PHP programs
Eight simple rules to writing secure PHP programsEight simple rules to writing secure PHP programs
Eight simple rules to writing secure PHP programs
 
Social media security challenges
Social media security challengesSocial media security challenges
Social media security challenges
 
Social Engineering and What to do About it
Social Engineering and What to do About itSocial Engineering and What to do About it
Social Engineering and What to do About it
 
OWASP Much ado about randomness
OWASP Much ado about randomnessOWASP Much ado about randomness
OWASP Much ado about randomness
 
Malware goes to the movies
Malware goes to the moviesMalware goes to the movies
Malware goes to the movies
 
Inoculation strategies for victims of viruses
Inoculation strategies for victims of virusesInoculation strategies for victims of viruses
Inoculation strategies for victims of viruses
 
Number theory lecture (part 1)
Number theory lecture (part 1)Number theory lecture (part 1)
Number theory lecture (part 1)
 
Number theory lecture (part 2)
Number theory lecture (part 2)Number theory lecture (part 2)
Number theory lecture (part 2)
 
Much ado about randomness. What is really a random number?
Much ado about randomness. What is really a random number?Much ado about randomness. What is really a random number?
Much ado about randomness. What is really a random number?
 
Secure information aggregation in sensor networks
Secure information aggregation in sensor networksSecure information aggregation in sensor networks
Secure information aggregation in sensor networks
 
A verifiable random function with short proofs and keys
A verifiable random function with short proofs and keysA verifiable random function with short proofs and keys
A verifiable random function with short proofs and keys
 
Price of anarchy is independent of network topology
Price of anarchy is independent of network topologyPrice of anarchy is independent of network topology
Price of anarchy is independent of network topology
 
Business Case Studies
Business Case Studies Business Case Studies
Business Case Studies
 
Spreading Rumors Quietly and the Subgroup Escape Problem
Spreading Rumors Quietly and the Subgroup Escape ProblemSpreading Rumors Quietly and the Subgroup Escape Problem
Spreading Rumors Quietly and the Subgroup Escape Problem
 

Theory of Data Entanglement Protects Remotely Stored Data

  • 1. Towards a Theory of Data Entanglement James Aspnes, Joan Feigenbaum, Aleksandr Yampolskiy, and Sheng Zhong (Yale University)
  • 2.
  • 3.
  • 4. Previous Work: Dagster [SW01] New Document  Encrypt c randomly chosen blocks Pool of blocks Analysis: Deleting a typical document  loss of O( c ) documents
  • 5. Previous Work: Tangler [WM01] (0, New Document) 2 randomly chosen blocks Pool of n blocks Analysis: Deleting a typical document  loss of O ( (log n ) / n ) documents Interpolate degree-2 poly F() (x 1 ,F(x 1 )) (x 2 ,F(x 2 ))
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20. Summary of Results  all-or-nothing Private Recovery symmetric recovery all-or-nothing Public Recovery all-or-nothing all-or-nothing Standard Recovery Arbitrary Tamperer Destructive Tamperer
  • 21.

Editor's Notes

  1. How does the tamperer work and who gives out recovery algorithms?
  2. Replace picture and possibly drop the formal definition
  3. Add a picture of happy/unhappy computers
  4. Add a picture of happy/unhappy computers
  5. Emphasize that adversary cannot guess what the k_i are. Many-to-one map can’t be correct for most k_i. b/c of the property of polynomials. Maybe add picture (?)