SlideShare a Scribd company logo
1 of 12
Information and Network Security:34
Primality
Prof Neeraj Bhargava
Vaibhav Khanna
Department of Computer Science
School of Engineering and Systems Sciences
Maharshi Dayanand Saraswati University Ajmer
Prime Numbers
prime numbers only have divisors of 1 and self
they cannot be written as a product of other numbers
note: 1 is prime, but is generally not of interest
eg. 2,3,5,7 are prime, 4,6,8,9,10 are not
prime numbers are central to number theory
list of prime number less than 200 is:
2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67 71 73 79
83 89 97 101 103 107 109 113 127 131 137 139 149 151 157 163
167 173 179 181 191 193 197 199
Prime Factorisation
to factor a number n is to write it as a product of other numbers:
n=a x b x c
note that factoring a number is relatively hard compared to
multiplying the factors together to generate the number
the prime factorisation of a number n is when its written as a
product of primes
eg. 91=7x13 ; 3600=24x32x52
• The idea of "factoring" a number is important - finding numbers
which divide into it.
• Taking this as far as can go, by factorising all the factors, we can
eventually write the number as a product of (powers of) primes - its
prime factorisation.
• Note also that factoring a number is relatively hard compared to
multiplying the factors together to generate the number.
Relatively Prime Numbers & GCD
• two numbers a, b are relatively prime if have no common divisors apart
from 1
• eg. 8 & 15 are relatively prime since factors of 8 are 1,2,4,8 and of 15 are 1,3,5,15
and 1 is the only common factor
• Have the concept of “relatively prime” if two number share no common
factors other than 1.
• Another common problem is to determine the "greatest common divisor”
GCD(a,b) which is the largest number that divides into both a & b.
• conversely can determine the greatest common divisor by comparing their
prime factorizations and using least powers
• eg. 300=21x31x52 18=21x32 hence GCD(18,300)=21x31x50=6
Fermat's Theorem
• ap-1 = 1 (mod p)
• where p is prime and gcd(a,p)=1
• also known as Fermat’s Little Theorem
• also have: ap = a (mod p)
• useful in public key and primality testing
• Two theorems that play important roles in public-key cryptography
are Fermat’s theorem and Euler’s theorem.
• Fermat’s theorem (also known as Fermat’s Little Theorem) as listed
above, states an important property of prime numbers.
Euler Totient Function ø(n)
• when doing arithmetic modulo n
• complete set of residues is: 0..n-1
• reduced set of residues is those numbers (residues) which are
relatively prime to n
• eg for n=10,
• complete set of residues is {0,1,2,3,4,5,6,7,8,9}
• reduced set of residues is {1,3,7,9}
• number of elements in reduced set of residues is called the Euler
Totient Function ø(n)
Euler Totient Function ø(n)
• to compute ø(n) need to count number of residues to be excluded
• in general need prime factorization, but
• for p (p prime) ø(p)=p-1
• for p.q (p,q prime) ø(p.q)=(p-1)x(q-1)
• compute ø(n) need to count the number of residues to be excluded.
In general you need use a complex formula on the prime factorization
of n, but have a couple of special cases as shown.
• eg.
ø(37) = 36
ø(21) = (3–1)x(7–1) = 2x6 = 12
Euler's Theorem
• a generalisation of Fermat's Theorem
• aø(n) = 1 (mod n)
• for any a,n where gcd(a,n)=1
• Euler's Theorem is a generalization of Fermat's Theorem for any number n.
• As is the case for Fermat's theorem, an alternative form of the theorem is also
useful. Again, similar to the case with Fermat's theorem, the first form of Euler's
theorem requires that a be relatively prime to n.
• eg.
a=3;n=10; ø(10)=4;
hence 34 = 81 = 1 mod 10
a=2;n=11; ø(11)=10;
hence 210 = 1024 = 1 mod 11
• also have: aø(n)+1 = a (mod n)
Primality Testing
often need to find large prime numbers
traditionally sieve using trial division
ie. divide by all numbers (primes) in turn less than the square root of the
number
only works for small numbers
alternatively can use statistical primality tests based on properties of
primes
for which all primes numbers satisfy property
but some composite numbers, called pseudo-primes, also satisfy the property
can use a slower deterministic primality test
• For many cryptographic functions it is necessary to select one or
more very large prime numbers at random. Thus we are faced with
the task of determining whether a given large number is prime.
• There is no simple yet efficient means of accomplishing this task.
• Traditionally sieve for primes using trial division of all possible prime
factors of some number, but this only works for small numbers.
• Alternatively can use repeated statistical primality tests based on
properties of primes, and then for certainty, use a slower
deterministic primality test, such as the AKS test.
Assignment
• Explain in detail the working of Primality Algorithm

More Related Content

What's hot

Greek logic and mathematics
Greek logic and mathematicsGreek logic and mathematics
Greek logic and mathematicsBob Marcus
 
Davonte M Equations
Davonte M EquationsDavonte M Equations
Davonte M Equationsdmartin
 
CaseWare Data Scientist test.
CaseWare Data Scientist test.CaseWare Data Scientist test.
CaseWare Data Scientist test.Aila Ansari
 
1.2 order of operations lesson
1.2 order of operations lesson1.2 order of operations lesson
1.2 order of operations lessongwilson8786
 
Zeros or roots of a polynomial if a greater than1
Zeros or roots of a polynomial if a greater than1Zeros or roots of a polynomial if a greater than1
Zeros or roots of a polynomial if a greater than1MartinGeraldine
 
An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-O...
An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-O...An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-O...
An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-O...Annibale Panichella
 
Matlab lecture 7 – regula falsi or false position method@taj
Matlab lecture 7 – regula falsi or false position method@tajMatlab lecture 7 – regula falsi or false position method@taj
Matlab lecture 7 – regula falsi or false position method@tajTajim Md. Niamat Ullah Akhund
 
5.1part2 foil
5.1part2 foil5.1part2 foil
5.1part2 foilvhiggins1
 
Module 2 Lesson 2 Notes
Module 2 Lesson 2 NotesModule 2 Lesson 2 Notes
Module 2 Lesson 2 Notestoni dimella
 

What's hot (20)

Calc 4.1a
Calc 4.1aCalc 4.1a
Calc 4.1a
 
Calc 4.1a
Calc 4.1aCalc 4.1a
Calc 4.1a
 
Calc 4.1a
Calc 4.1aCalc 4.1a
Calc 4.1a
 
Greek logic and mathematics
Greek logic and mathematicsGreek logic and mathematics
Greek logic and mathematics
 
Davonte M Equations
Davonte M EquationsDavonte M Equations
Davonte M Equations
 
CaseWare Data Scientist test.
CaseWare Data Scientist test.CaseWare Data Scientist test.
CaseWare Data Scientist test.
 
1.2 order of operations lesson
1.2 order of operations lesson1.2 order of operations lesson
1.2 order of operations lesson
 
Basic Integral
Basic IntegralBasic Integral
Basic Integral
 
Uas teori bil.
Uas teori bil.Uas teori bil.
Uas teori bil.
 
Zeros or roots of a polynomial if a greater than1
Zeros or roots of a polynomial if a greater than1Zeros or roots of a polynomial if a greater than1
Zeros or roots of a polynomial if a greater than1
 
Polynomials
PolynomialsPolynomials
Polynomials
 
An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-O...
An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-O...An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-O...
An Adaptive Evolutionary Algorithm based on Non-Euclidean Geometry for Many-O...
 
Unit .5
Unit .5Unit .5
Unit .5
 
Bisection
BisectionBisection
Bisection
 
Pre-Cal 40S June 3, 2009
Pre-Cal 40S June 3, 2009Pre-Cal 40S June 3, 2009
Pre-Cal 40S June 3, 2009
 
Matlab lecture 7 – regula falsi or false position method@taj
Matlab lecture 7 – regula falsi or false position method@tajMatlab lecture 7 – regula falsi or false position method@taj
Matlab lecture 7 – regula falsi or false position method@taj
 
5.1part2 foil
5.1part2 foil5.1part2 foil
5.1part2 foil
 
Module 2 Lesson 2 Notes
Module 2 Lesson 2 NotesModule 2 Lesson 2 Notes
Module 2 Lesson 2 Notes
 
Lec 06
Lec 06Lec 06
Lec 06
 
Mean-median-mode
Mean-median-modeMean-median-mode
Mean-median-mode
 

Similar to Information and network security 34 primality

2010 3-24 cryptography stamatiou
2010 3-24 cryptography stamatiou2010 3-24 cryptography stamatiou
2010 3-24 cryptography stamatiouvafopoulos
 
ch08 modified.pptmodified.pptmodified.ppt
ch08 modified.pptmodified.pptmodified.pptch08 modified.pptmodified.pptmodified.ppt
ch08 modified.pptmodified.pptmodified.ppttahirnaquash2
 
Prime Numbers and Their Digital Roots
Prime Numbers and Their Digital RootsPrime Numbers and Their Digital Roots
Prime Numbers and Their Digital RootsIRJET Journal
 
Cyber Security Part-3.pptx
Cyber Security Part-3.pptxCyber Security Part-3.pptx
Cyber Security Part-3.pptxRavikumarVadana
 
Basics of Mathematical Cryptography
Basics of Mathematical CryptographyBasics of Mathematical Cryptography
Basics of Mathematical CryptographyNeha Gupta
 
Information and network security 33 rsa algorithm
Information and network security 33 rsa algorithmInformation and network security 33 rsa algorithm
Information and network security 33 rsa algorithmVaibhav Khanna
 
Chapter 4 Review Part 1
Chapter 4 Review Part 1Chapter 4 Review Part 1
Chapter 4 Review Part 1Jessca Lundin
 
RSA final notation change2
RSA final notation change2RSA final notation change2
RSA final notation change2Coleman Gorham
 

Similar to Information and network security 34 primality (20)

Eulers totient
Eulers totientEulers totient
Eulers totient
 
Unit 3.ppt
Unit 3.pptUnit 3.ppt
Unit 3.ppt
 
Ch08
Ch08Ch08
Ch08
 
2010 3-24 cryptography stamatiou
2010 3-24 cryptography stamatiou2010 3-24 cryptography stamatiou
2010 3-24 cryptography stamatiou
 
ch08 modified.pptmodified.pptmodified.ppt
ch08 modified.pptmodified.pptmodified.pptch08 modified.pptmodified.pptmodified.ppt
ch08 modified.pptmodified.pptmodified.ppt
 
Primality
PrimalityPrimality
Primality
 
Public Key and RSA.pdf
Public Key and RSA.pdfPublic Key and RSA.pdf
Public Key and RSA.pdf
 
Prime Numbers and Their Digital Roots
Prime Numbers and Their Digital RootsPrime Numbers and Their Digital Roots
Prime Numbers and Their Digital Roots
 
Cyber Security Part-3.pptx
Cyber Security Part-3.pptxCyber Security Part-3.pptx
Cyber Security Part-3.pptx
 
Basics of Mathematical Cryptography
Basics of Mathematical CryptographyBasics of Mathematical Cryptography
Basics of Mathematical Cryptography
 
RSA
RSARSA
RSA
 
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applie...
 
RSA ALGORITHM
RSA ALGORITHMRSA ALGORITHM
RSA ALGORITHM
 
Information and network security 33 rsa algorithm
Information and network security 33 rsa algorithmInformation and network security 33 rsa algorithm
Information and network security 33 rsa algorithm
 
MUMS: Bayesian, Fiducial, and Frequentist Conference - Objective Bayesian Ana...
MUMS: Bayesian, Fiducial, and Frequentist Conference - Objective Bayesian Ana...MUMS: Bayesian, Fiducial, and Frequentist Conference - Objective Bayesian Ana...
MUMS: Bayesian, Fiducial, and Frequentist Conference - Objective Bayesian Ana...
 
Unit 1
Unit 1Unit 1
Unit 1
 
Slides to RSA Presentation
Slides to RSA PresentationSlides to RSA Presentation
Slides to RSA Presentation
 
Chapter 4 Review Part 1
Chapter 4 Review Part 1Chapter 4 Review Part 1
Chapter 4 Review Part 1
 
Daa notes 2
Daa notes 2Daa notes 2
Daa notes 2
 
RSA final notation change2
RSA final notation change2RSA final notation change2
RSA final notation change2
 

More from Vaibhav Khanna

Information and network security 47 authentication applications
Information and network security 47 authentication applicationsInformation and network security 47 authentication applications
Information and network security 47 authentication applicationsVaibhav Khanna
 
Information and network security 46 digital signature algorithm
Information and network security 46 digital signature algorithmInformation and network security 46 digital signature algorithm
Information and network security 46 digital signature algorithmVaibhav Khanna
 
Information and network security 45 digital signature standard
Information and network security 45 digital signature standardInformation and network security 45 digital signature standard
Information and network security 45 digital signature standardVaibhav Khanna
 
Information and network security 44 direct digital signatures
Information and network security 44 direct digital signaturesInformation and network security 44 direct digital signatures
Information and network security 44 direct digital signaturesVaibhav Khanna
 
Information and network security 43 digital signatures
Information and network security 43 digital signaturesInformation and network security 43 digital signatures
Information and network security 43 digital signaturesVaibhav Khanna
 
Information and network security 42 security of message authentication code
Information and network security 42 security of message authentication codeInformation and network security 42 security of message authentication code
Information and network security 42 security of message authentication codeVaibhav Khanna
 
Information and network security 41 message authentication code
Information and network security 41 message authentication codeInformation and network security 41 message authentication code
Information and network security 41 message authentication codeVaibhav Khanna
 
Information and network security 40 sha3 secure hash algorithm
Information and network security 40 sha3 secure hash algorithmInformation and network security 40 sha3 secure hash algorithm
Information and network security 40 sha3 secure hash algorithmVaibhav Khanna
 
Information and network security 39 secure hash algorithm
Information and network security 39 secure hash algorithmInformation and network security 39 secure hash algorithm
Information and network security 39 secure hash algorithmVaibhav Khanna
 
Information and network security 38 birthday attacks and security of hash fun...
Information and network security 38 birthday attacks and security of hash fun...Information and network security 38 birthday attacks and security of hash fun...
Information and network security 38 birthday attacks and security of hash fun...Vaibhav Khanna
 
Information and network security 37 hash functions and message authentication
Information and network security 37 hash functions and message authenticationInformation and network security 37 hash functions and message authentication
Information and network security 37 hash functions and message authenticationVaibhav Khanna
 
Information and network security 35 the chinese remainder theorem
Information and network security 35 the chinese remainder theoremInformation and network security 35 the chinese remainder theorem
Information and network security 35 the chinese remainder theoremVaibhav Khanna
 
Information and network security 32 principles of public key cryptosystems
Information and network security 32 principles of public key cryptosystemsInformation and network security 32 principles of public key cryptosystems
Information and network security 32 principles of public key cryptosystemsVaibhav Khanna
 
Information and network security 31 public key cryptography
Information and network security 31 public key cryptographyInformation and network security 31 public key cryptography
Information and network security 31 public key cryptographyVaibhav Khanna
 
Information and network security 30 random numbers
Information and network security 30 random numbersInformation and network security 30 random numbers
Information and network security 30 random numbersVaibhav Khanna
 
Information and network security 29 international data encryption algorithm
Information and network security 29 international data encryption algorithmInformation and network security 29 international data encryption algorithm
Information and network security 29 international data encryption algorithmVaibhav Khanna
 
Information and network security 28 blowfish
Information and network security 28 blowfishInformation and network security 28 blowfish
Information and network security 28 blowfishVaibhav Khanna
 
Information and network security 27 triple des
Information and network security 27 triple desInformation and network security 27 triple des
Information and network security 27 triple desVaibhav Khanna
 
Information and network security 26 aes decryption and implementational issues
Information and network security 26 aes decryption and implementational issuesInformation and network security 26 aes decryption and implementational issues
Information and network security 26 aes decryption and implementational issuesVaibhav Khanna
 
Information and network security 25 algorithmic steps of aes
Information and network security 25 algorithmic steps of aesInformation and network security 25 algorithmic steps of aes
Information and network security 25 algorithmic steps of aesVaibhav Khanna
 

More from Vaibhav Khanna (20)

Information and network security 47 authentication applications
Information and network security 47 authentication applicationsInformation and network security 47 authentication applications
Information and network security 47 authentication applications
 
Information and network security 46 digital signature algorithm
Information and network security 46 digital signature algorithmInformation and network security 46 digital signature algorithm
Information and network security 46 digital signature algorithm
 
Information and network security 45 digital signature standard
Information and network security 45 digital signature standardInformation and network security 45 digital signature standard
Information and network security 45 digital signature standard
 
Information and network security 44 direct digital signatures
Information and network security 44 direct digital signaturesInformation and network security 44 direct digital signatures
Information and network security 44 direct digital signatures
 
Information and network security 43 digital signatures
Information and network security 43 digital signaturesInformation and network security 43 digital signatures
Information and network security 43 digital signatures
 
Information and network security 42 security of message authentication code
Information and network security 42 security of message authentication codeInformation and network security 42 security of message authentication code
Information and network security 42 security of message authentication code
 
Information and network security 41 message authentication code
Information and network security 41 message authentication codeInformation and network security 41 message authentication code
Information and network security 41 message authentication code
 
Information and network security 40 sha3 secure hash algorithm
Information and network security 40 sha3 secure hash algorithmInformation and network security 40 sha3 secure hash algorithm
Information and network security 40 sha3 secure hash algorithm
 
Information and network security 39 secure hash algorithm
Information and network security 39 secure hash algorithmInformation and network security 39 secure hash algorithm
Information and network security 39 secure hash algorithm
 
Information and network security 38 birthday attacks and security of hash fun...
Information and network security 38 birthday attacks and security of hash fun...Information and network security 38 birthday attacks and security of hash fun...
Information and network security 38 birthday attacks and security of hash fun...
 
Information and network security 37 hash functions and message authentication
Information and network security 37 hash functions and message authenticationInformation and network security 37 hash functions and message authentication
Information and network security 37 hash functions and message authentication
 
Information and network security 35 the chinese remainder theorem
Information and network security 35 the chinese remainder theoremInformation and network security 35 the chinese remainder theorem
Information and network security 35 the chinese remainder theorem
 
Information and network security 32 principles of public key cryptosystems
Information and network security 32 principles of public key cryptosystemsInformation and network security 32 principles of public key cryptosystems
Information and network security 32 principles of public key cryptosystems
 
Information and network security 31 public key cryptography
Information and network security 31 public key cryptographyInformation and network security 31 public key cryptography
Information and network security 31 public key cryptography
 
Information and network security 30 random numbers
Information and network security 30 random numbersInformation and network security 30 random numbers
Information and network security 30 random numbers
 
Information and network security 29 international data encryption algorithm
Information and network security 29 international data encryption algorithmInformation and network security 29 international data encryption algorithm
Information and network security 29 international data encryption algorithm
 
Information and network security 28 blowfish
Information and network security 28 blowfishInformation and network security 28 blowfish
Information and network security 28 blowfish
 
Information and network security 27 triple des
Information and network security 27 triple desInformation and network security 27 triple des
Information and network security 27 triple des
 
Information and network security 26 aes decryption and implementational issues
Information and network security 26 aes decryption and implementational issuesInformation and network security 26 aes decryption and implementational issues
Information and network security 26 aes decryption and implementational issues
 
Information and network security 25 algorithmic steps of aes
Information and network security 25 algorithmic steps of aesInformation and network security 25 algorithmic steps of aes
Information and network security 25 algorithmic steps of aes
 

Recently uploaded

Optimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVOptimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVshikhaohhpro
 
Introduction to Decentralized Applications (dApps)
Introduction to Decentralized Applications (dApps)Introduction to Decentralized Applications (dApps)
Introduction to Decentralized Applications (dApps)Intelisync
 
why an Opensea Clone Script might be your perfect match.pdf
why an Opensea Clone Script might be your perfect match.pdfwhy an Opensea Clone Script might be your perfect match.pdf
why an Opensea Clone Script might be your perfect match.pdfjoe51371421
 
Adobe Marketo Engage Deep Dives: Using Webhooks to Transfer Data
Adobe Marketo Engage Deep Dives: Using Webhooks to Transfer DataAdobe Marketo Engage Deep Dives: Using Webhooks to Transfer Data
Adobe Marketo Engage Deep Dives: Using Webhooks to Transfer DataBradBedford3
 
Cloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackCloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackVICTOR MAESTRE RAMIREZ
 
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdfThe Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdfkalichargn70th171
 
DNT_Corporate presentation know about us
DNT_Corporate presentation know about usDNT_Corporate presentation know about us
DNT_Corporate presentation know about usDynamic Netsoft
 
Unit 1.1 Excite Part 1, class 9, cbse...
Unit 1.1 Excite Part 1, class 9, cbse...Unit 1.1 Excite Part 1, class 9, cbse...
Unit 1.1 Excite Part 1, class 9, cbse...aditisharan08
 
Advancing Engineering with AI through the Next Generation of Strategic Projec...
Advancing Engineering with AI through the Next Generation of Strategic Projec...Advancing Engineering with AI through the Next Generation of Strategic Projec...
Advancing Engineering with AI through the Next Generation of Strategic Projec...OnePlan Solutions
 
The Ultimate Test Automation Guide_ Best Practices and Tips.pdf
The Ultimate Test Automation Guide_ Best Practices and Tips.pdfThe Ultimate Test Automation Guide_ Best Practices and Tips.pdf
The Ultimate Test Automation Guide_ Best Practices and Tips.pdfkalichargn70th171
 
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed DataAlluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed DataAlluxio, Inc.
 
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...stazi3110
 
EY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityEY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityNeo4j
 
Unlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language ModelsUnlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language Modelsaagamshah0812
 
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...ICS
 
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...kellynguyen01
 
chapter--4-software-project-planning.ppt
chapter--4-software-project-planning.pptchapter--4-software-project-planning.ppt
chapter--4-software-project-planning.pptkotipi9215
 
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...harshavardhanraghave
 

Recently uploaded (20)

Optimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTVOptimizing AI for immediate response in Smart CCTV
Optimizing AI for immediate response in Smart CCTV
 
Introduction to Decentralized Applications (dApps)
Introduction to Decentralized Applications (dApps)Introduction to Decentralized Applications (dApps)
Introduction to Decentralized Applications (dApps)
 
why an Opensea Clone Script might be your perfect match.pdf
why an Opensea Clone Script might be your perfect match.pdfwhy an Opensea Clone Script might be your perfect match.pdf
why an Opensea Clone Script might be your perfect match.pdf
 
Adobe Marketo Engage Deep Dives: Using Webhooks to Transfer Data
Adobe Marketo Engage Deep Dives: Using Webhooks to Transfer DataAdobe Marketo Engage Deep Dives: Using Webhooks to Transfer Data
Adobe Marketo Engage Deep Dives: Using Webhooks to Transfer Data
 
Call Girls In Mukherjee Nagar 📱 9999965857 🤩 Delhi 🫦 HOT AND SEXY VVIP 🍎 SE...
Call Girls In Mukherjee Nagar 📱  9999965857  🤩 Delhi 🫦 HOT AND SEXY VVIP 🍎 SE...Call Girls In Mukherjee Nagar 📱  9999965857  🤩 Delhi 🫦 HOT AND SEXY VVIP 🍎 SE...
Call Girls In Mukherjee Nagar 📱 9999965857 🤩 Delhi 🫦 HOT AND SEXY VVIP 🍎 SE...
 
Cloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStackCloud Management Software Platforms: OpenStack
Cloud Management Software Platforms: OpenStack
 
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdfThe Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
The Essentials of Digital Experience Monitoring_ A Comprehensive Guide.pdf
 
DNT_Corporate presentation know about us
DNT_Corporate presentation know about usDNT_Corporate presentation know about us
DNT_Corporate presentation know about us
 
Unit 1.1 Excite Part 1, class 9, cbse...
Unit 1.1 Excite Part 1, class 9, cbse...Unit 1.1 Excite Part 1, class 9, cbse...
Unit 1.1 Excite Part 1, class 9, cbse...
 
Advancing Engineering with AI through the Next Generation of Strategic Projec...
Advancing Engineering with AI through the Next Generation of Strategic Projec...Advancing Engineering with AI through the Next Generation of Strategic Projec...
Advancing Engineering with AI through the Next Generation of Strategic Projec...
 
The Ultimate Test Automation Guide_ Best Practices and Tips.pdf
The Ultimate Test Automation Guide_ Best Practices and Tips.pdfThe Ultimate Test Automation Guide_ Best Practices and Tips.pdf
The Ultimate Test Automation Guide_ Best Practices and Tips.pdf
 
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed DataAlluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
Alluxio Monthly Webinar | Cloud-Native Model Training on Distributed Data
 
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
Building a General PDE Solving Framework with Symbolic-Numeric Scientific Mac...
 
EY_Graph Database Powered Sustainability
EY_Graph Database Powered SustainabilityEY_Graph Database Powered Sustainability
EY_Graph Database Powered Sustainability
 
Unlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language ModelsUnlocking the Future of AI Agents with Large Language Models
Unlocking the Future of AI Agents with Large Language Models
 
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...
 
Exploring iOS App Development: Simplifying the Process
Exploring iOS App Development: Simplifying the ProcessExploring iOS App Development: Simplifying the Process
Exploring iOS App Development: Simplifying the Process
 
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
Short Story: Unveiling the Reasoning Abilities of Large Language Models by Ke...
 
chapter--4-software-project-planning.ppt
chapter--4-software-project-planning.pptchapter--4-software-project-planning.ppt
chapter--4-software-project-planning.ppt
 
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...
 

Information and network security 34 primality

  • 1. Information and Network Security:34 Primality Prof Neeraj Bhargava Vaibhav Khanna Department of Computer Science School of Engineering and Systems Sciences Maharshi Dayanand Saraswati University Ajmer
  • 2. Prime Numbers prime numbers only have divisors of 1 and self they cannot be written as a product of other numbers note: 1 is prime, but is generally not of interest eg. 2,3,5,7 are prime, 4,6,8,9,10 are not prime numbers are central to number theory list of prime number less than 200 is: 2 3 5 7 11 13 17 19 23 29 31 37 41 43 47 53 59 61 67 71 73 79 83 89 97 101 103 107 109 113 127 131 137 139 149 151 157 163 167 173 179 181 191 193 197 199
  • 3. Prime Factorisation to factor a number n is to write it as a product of other numbers: n=a x b x c note that factoring a number is relatively hard compared to multiplying the factors together to generate the number the prime factorisation of a number n is when its written as a product of primes eg. 91=7x13 ; 3600=24x32x52
  • 4. • The idea of "factoring" a number is important - finding numbers which divide into it. • Taking this as far as can go, by factorising all the factors, we can eventually write the number as a product of (powers of) primes - its prime factorisation. • Note also that factoring a number is relatively hard compared to multiplying the factors together to generate the number.
  • 5. Relatively Prime Numbers & GCD • two numbers a, b are relatively prime if have no common divisors apart from 1 • eg. 8 & 15 are relatively prime since factors of 8 are 1,2,4,8 and of 15 are 1,3,5,15 and 1 is the only common factor • Have the concept of “relatively prime” if two number share no common factors other than 1. • Another common problem is to determine the "greatest common divisor” GCD(a,b) which is the largest number that divides into both a & b. • conversely can determine the greatest common divisor by comparing their prime factorizations and using least powers • eg. 300=21x31x52 18=21x32 hence GCD(18,300)=21x31x50=6
  • 6. Fermat's Theorem • ap-1 = 1 (mod p) • where p is prime and gcd(a,p)=1 • also known as Fermat’s Little Theorem • also have: ap = a (mod p) • useful in public key and primality testing • Two theorems that play important roles in public-key cryptography are Fermat’s theorem and Euler’s theorem. • Fermat’s theorem (also known as Fermat’s Little Theorem) as listed above, states an important property of prime numbers.
  • 7. Euler Totient Function ø(n) • when doing arithmetic modulo n • complete set of residues is: 0..n-1 • reduced set of residues is those numbers (residues) which are relatively prime to n • eg for n=10, • complete set of residues is {0,1,2,3,4,5,6,7,8,9} • reduced set of residues is {1,3,7,9} • number of elements in reduced set of residues is called the Euler Totient Function ø(n)
  • 8. Euler Totient Function ø(n) • to compute ø(n) need to count number of residues to be excluded • in general need prime factorization, but • for p (p prime) ø(p)=p-1 • for p.q (p,q prime) ø(p.q)=(p-1)x(q-1) • compute ø(n) need to count the number of residues to be excluded. In general you need use a complex formula on the prime factorization of n, but have a couple of special cases as shown. • eg. ø(37) = 36 ø(21) = (3–1)x(7–1) = 2x6 = 12
  • 9. Euler's Theorem • a generalisation of Fermat's Theorem • aø(n) = 1 (mod n) • for any a,n where gcd(a,n)=1 • Euler's Theorem is a generalization of Fermat's Theorem for any number n. • As is the case for Fermat's theorem, an alternative form of the theorem is also useful. Again, similar to the case with Fermat's theorem, the first form of Euler's theorem requires that a be relatively prime to n. • eg. a=3;n=10; ø(10)=4; hence 34 = 81 = 1 mod 10 a=2;n=11; ø(11)=10; hence 210 = 1024 = 1 mod 11 • also have: aø(n)+1 = a (mod n)
  • 10. Primality Testing often need to find large prime numbers traditionally sieve using trial division ie. divide by all numbers (primes) in turn less than the square root of the number only works for small numbers alternatively can use statistical primality tests based on properties of primes for which all primes numbers satisfy property but some composite numbers, called pseudo-primes, also satisfy the property can use a slower deterministic primality test
  • 11. • For many cryptographic functions it is necessary to select one or more very large prime numbers at random. Thus we are faced with the task of determining whether a given large number is prime. • There is no simple yet efficient means of accomplishing this task. • Traditionally sieve for primes using trial division of all possible prime factors of some number, but this only works for small numbers. • Alternatively can use repeated statistical primality tests based on properties of primes, and then for certainty, use a slower deterministic primality test, such as the AKS test.
  • 12. Assignment • Explain in detail the working of Primality Algorithm