This document provides an overview of digital fingerprinting technology. It explains that digital fingerprinting can automatically identify phrases from confidential documents that appear in analyzed texts. The technology works by analyzing media like songs or videos to identify unique portions that can be matched to a database. Major companies like YouTube and Audible Magic use digital fingerprinting to identify copyrighted content and prevent unauthorized sharing. The document also discusses watermarking and how fingerprinting offers a more effective way to restrict copyrighted material than visible watermarks.

1. DIGITAL
FINGERPRINTING
PRESENTED BY
DHARAMSOTH SANTHOSH

2. What is digital fingerprinting???
• Digital fingerprint technology is designed to protect large
documents, the contents of which do not change, or
change little.
• The technique used to detect digital fingerprints can
automatically identify phrases from sample documents
containing confidential information that appears in an
analyzed text.

3. How digital fingerprinting
works???
• Suppose your favorite song suddenly pops into your
head what will you do??

4. Continue….
• Digital fingerprinting technology relies on complex
computer-driven analysis to identify a piece of media like
a song or video clip.
• Just like every person has a unique fingerprint, every
piece of media has identifying features that can be
spotted by smart software.
• Sites like YouTube can scan files and match their
fingerprints against a database of copyrighted material
and stop users from uploading copyrighted files.

5. Watermarking vs Fingerprinting
• These are two very different technologies with somewhat
similar goals.
• A watermark is a logo or other identifying marking
placed on an image or video that is visible at all times.
• Pirates can still share watermarked videos, and some
photos with smaller watermarks can easily be cropped to
hide the identity of rightful owner.
• Digital fingerprinting offers an even more promising way
to restrict the spread of copyrighted material.

6. Digital fingerprinting technology
• For fingerprinting to work, software has to be able to
accurately identify a piece of media and relate that file
to an external database.
• Fingerprinting software samples of an audio or video file
to pick out tiny portions of the file that are unique to
that piece of media.
• One major digital fingerprinting company, Audible Magic
works for NBC universal, sony music and 20th century
fox.

7. Continue…
• Audible magic copy sense technology can identify the
source of video clip in 5 seconds and audio file in 10
seconds.
• Software can recognize a piece of media that was, say,
recorded off a movie theater with a handheld camera.
• It cannot identify every piece of content on internet it
will identify which is present in its database.

8. Reasons for digital fingerprinting
• In 2007 Viacom sued Google for $1billion overs clips
available on YouTube.
• With digital fingerprinting, Google uses software it calls
YouTube Video Identification to sort through uploaded
videos and recognize copyright content.
• It protects Google from harmful lawsuits and limits the
unlicensed spread of copyrighted material.
• Another excellent example of fingerprinting at work is
shazam, it is music identification app that can match a
songs audio sample to a musical database.

9. Effectiveness of digital
fingerprinting
• Digital fingerprinting sounds like the perfect technology to
combat internet privacy.
• Digital fingerprinting must be able to identify thousands
or millions of pieces of content- content that can be
disseminated in many media formants or even recorded
off a movie theatre screen.
• Audible magic protects against 11 million songs, movies
and television shows.

10. Homomorphic Encryption

11. Introduction
• Homomorphic encryption is a form of encryption which
allows specific types of computations to be carried out
on cipher text and generate an encrypted result which,
when decrypted, matches the result of operations
performed on the plain text.
• For instance, one person could add two encrypted numbers and then
another person could decrypt the result, without either of them being able
to find the value of the individual numbers.

12. • Earlier there was Somewhat Homomorphic Encryption technique. This
encryption used low polynomial degree, which was its big drawback.
• In June 2009, “Gentry” proposed the first efficient Fully Homomorphic
Encryption technique. It is efficient in the sense that all algorithms run in
polynomial time.

13. An Analogy: Alice’s Jewellery Store
• Alice’s workers need to assemble raw materials into jewellery
• But Alice is worried about theft
How can the workers process the raw materials without having access to them?

14. • Alice puts materials in locked glove box
• For which only she has the key
• Workers assemble jewellery in the box
• Alice unlocks box to get “results

15. Why homomorphic encryption?
• Proposed by Rivest, Adleman and Dertouzos
• Confidentiality problems
• Ability to compute over cipher text instead of plaintext
• One could use information without knowing the content
of that information
• Privacy guaranted

16. Homomorphic Encryption
• It is of two types:
• 1. partially homomorphic
• 2. fully homomorphic

17. Partially homomorphic schemes
• RSA: CT(x)*CT(y)=(x^e mod M)*(y^e mod M)=x^e*y^e mod
M=CT(x*y)
• Where ‘e’ is the exponent key and M the modulus
• We take an example:-
• p=61, q=53; n=pq=3233; ø(n)=(p-1)*(q-1)=60*52=3120;
• e=17; d=2753; d=e^(-1)mod ø(n)=17^(-1)mod 3120
• Here e is kept as public key exponent and d is kept
as private key exponent.

18. Continued..
• RSA: obtain 5*6 performing RSA(5)*RSA(6)
• RSA(5)= 5^17(mod 3233)=3086;
• RSA(6)=6^17(mod 3233)=824;
• 3086*824=2542864;
• RSA^-1(2542864)=2542864^2753(mod 3233)=30;
• 5*6=30

19. Fully homomorphic schemes
• We consider CRAIG GENTRY scheme.
• Suppose a scheme with a “noise parameter” attached to each
CT;
• Encryption algorithm outputs a CT with a small noise
parameter(say less than n);
• Decryption algorithm only works if noise is less than some
parameters N>>n;
• To compute E(a+b)/E(a*b), include noise;
• This gives a “somewhat homomorphic” scheme.

20. CRAIG GENTRY scheme
• Now suppose a new algorithm RECRYPT, such that:
• - input: E(a), with noise N’<n
• - output: E’(a), with noise√N
• “somewhat homomorphic” -> fully homomorphic
• Apply RECRYPT to E(a) and E(b) to ensure that the
noise in E(a*b) or E(a+b) is smaller than N.

21. CRAIG GENTRY
scheme(integers)
• KEY: odd integers p>2N
• ENCRYPTION ALGORITHM: given a bit b-> E(b)=c=
b+2x+kp, where x is in [-n/2,n/2] and k is an integer
chosen from some range
• DECRYPTION ALGORITHM: b=(c mod p) mod 2, where
(c mod p) is the noise and belongs to [-n,n]
• Decryption works if b+2x ∈ [-N,N] ⊂[-p/2,p/2]

22. CRAIG GENTRY
scheme(integers)
• Graig Gentry scheme’s homomorphic assumptions
• Addiction: c1 + c2 = b1+ b2 + 2(x1+x2) + (k1+k2)p = b1 xor b2 +
2x + kp
• Decryption works if (b1+2x1) + (b2+2x2) is in
[-N,N]
• Multiplication: c1*c2 = b1*b2 + 2(b1x2 + b2x1 + 2x1x2) + kp =
b1*b2 + 2x + kp
• Decryption works if (b1+2x1) * (b2+2x2) is in
[-N,N]

23. Craig Gentry scheme (integers)
• Addition example: 4+4
• CT(100):
• CT(1) = 1 + 2*3 + 5*3 = 22
• CT(0) = 0 + 2*3 + 5*3 = 21
• CT(0) = 0 + 2*3 + 5*3 = 21
• D(44 42 42):
• D(44) = 44 mod 3 = 2
• D(42) = 42 mod 3 = 0
• D(42) = 42 mod 3 = 0
22 21
21
+22 21 21
________
44 42
42
_________
1000 =8
=4+4

24. Craig Gentry scheme (integers)
• Multiplication example: 4*4
• CT(100):
• CT(1) = 1 + 2*3 + 5*3 = 22
• CT(0) = 0 + 2*3 + 5*3 = 21
• CT(0) = 0 + 2*3 + 5*3 = 21
• D(484 924 1365 882 441):
• D(484) = 484 mod 3 = 1
• D(924) = 924 mod 3 = 0
• D(1365) = 1365 mod 3 = 0
• D(882) = 882 mod 3 = 0
• D(441) = 441 mod 3 = 0
22 21 21
×22 21 21
--------------------------
484 924 1365 882 441
---------------------------
10000 =16=4*4

25. Why inefficient?
• CT size and computation time increase sharply as the security
level increases;
• 2k security -> CT size and computation time are high-degree
polynomials in k;
• Efforts are being made to reduce the computational requirements
of Craig Gentry construction
• Noise grows with each operation
• Threat for increasing cybercrimes through encrypted malwares

26. Applications
• Nowadays:
• Craig Gentry presented a working implementation of the fully
homomorphic system, including the bootstrapping function
• Exists a practical application of homomorphic encryption to a
hybrid wireless network
• Perform statistical tests over encrypted data such as temperature,
humidity, etc.
• There are also some practical implementations of simplifications
of this scheme over databases

27. Problems solved
• Cloud security
• Problems related to personal records like medical records
• Work with information stored in databases
• Query's to search engines