# Tag Archives: cryptography

## Revealing zero in fully homomorphic encryption is a Bad Thing

When I was first learning number theory, cryptography seemed really fun and really practical. I thought elementary number theory was elegant, and that cryptography was an elegant application. As I continued to learn more about mathematics, and in particular modern mathematics, I began to realize that decades of instruction and improvement (and perhaps of more useful points of view) have simplified the presentation of elementary number theory, and that modern mathematics is less elegant in presentation.

Similarly, as I learned more about cryptography, I learned that though the basic ideas are very simple, their application is often very inelegant. For example, the basis of RSA follows immediately from Euler’s Theorem as learned while studying elementary number theory, or alternately from Lagrange’s Theorem as learned while studying group theory or abstract algebra. And further, these are very early topics in these two areas of study!

But a naive implementation of RSA is doomed (For that matter, many professional implementations have their flaws too). Every now and then, a very clever expert comes up with a new attack on popular cryptosystems, generating new guidelines and recommendations. Some guidelines make intuitive sense [e.g. don’t use too small of an exponent for either the public or secret keys in RSA], but many are more complicated or designed to prevent more sophisticated attacks [especially side-channel attacks].

In the summer of 2013, I participated in the ICERM IdeaLab working towards more efficient homomorphic encryption. We were playing with existing homomorphic encryption schemes and trying to come up with new methods. One guideline that we followed is that an attacker should not be able to recognize an encryption of zero. This seems like a reasonable guideline, but I didn’t really understand why, until I was chatting with others at the 2017 Joint Mathematics Meetings in Atlanta.

It turns out that revealing zero isn’t just against generally sound advice. Revealing zero is a capital B capital T Bad Thing.

## Basic Setup

For the rest of this note, I’ll try to identify some of this reasoning.

In a typical cryptosystem, the basic setup is as follows. Andrew has a message that he wants to send to Beatrice. So Andrew converts the message into a list of numbers $M$, and uses some sort of encryption function $E(\cdot)$ to encrypt $M$, forming a ciphertext $C$. We can represent this as $C = E(M)$. Andrew transmits $C$ to Beatrice. If an eavesdropper Eve happens to intercept $C$, it should be very hard for Eve to recover any information about the original message from $C$. But when Beatrice receives $C$, she uses a corresponding decryption function $D(\cdot)$ to decrypt $C$, $M = d(C)$.

Often, the encryption and decryption techniques are based on number theoretic or combinatorial primitives. Some of these have extra structure (or at least they do with basic implementation). For instance, the RSA cryptosystem involves a public exponent $e$, a public mod $N$, and a private exponent $d$. Andrew encrypts the message $M$ by computing $C = E(M) \equiv M^e \bmod N$. Beatrice decrypts the message by computing $M = C^d \equiv M^{ed} \bmod N$.

Notice that in the RSA system, given two messages $M_1, M_2$ and corresponding ciphertexts $C_1, C_2$, we have that

E(M_1 M_2) \equiv (M_1 M_2)^e \equiv M_1^e M_2^e \equiv E(M_1) E(M_2) \pmod N. \notag

The encryption function $E(\cdot)$ is a group homomorphism. This is an example of extra structure.

A fully homomorphic cryptosystem has an encryption function $E(\cdot)$ satisfying both $E(M_1 + M_2) = E(M_1) + E(M_2)$ and $E(M_1M_2) = E(M_1)E(M_2)$ (or more generally an analogous pair of operations). That is, $E(\cdot)$ is a ring homomorphism.

This extra structure allows for (a lot of) extra utility. A fully homomorphic $E(\cdot)$ would allow one to perform meaningful operations on encrypted data, even though you can’t read the data itself. For example, a clinic could store (encrypted) medical information on an external server. A doctor or nurse could pull out a cellphone or tablet with relatively little computing power or memory and securely query the medical data. Fully homomorphic encryption would allow one to securely outsource data infrastructure.

A different usage model suggests that we use a different mental model. So suppose Alice has sensitive data that she wants to store for use on EveCorp’s servers. Alice knows an encryption method $E(\cdot)$ and a decryption method $D(\cdot)$, while EveCorp only ever has mountains of ciphertexts, and cannot read the data [even though they have it].

## Why revealing zero is a Bad Thing

Let us now consider some basic cryptographic attacks. We should assume that EveCorp has access to a long list of plaintext messages $M_i$ and their corresponding ciphertexts $C_i$. Not everything, but perhaps from small leaks or other avenues. Among the messages $M_i$ it is very likely that there are two messages $M_1, M_2$ which are relatively prime. Then an application of the Euclidean Algorithm gives a linear combination of $M_1$ and $M_2$ such that

M_1 x + M_2 y = 1 \notag

for some integers $x,y$. Even though EveCorp doesn’t know the encryption method $E(\cdot)$, since we are assuming that they have access to the corresponding ciphertexts $C_1$ and $C_2$, EveCorp has access to an encryption of $1$ using the ring homomorphism properties:
\label{eq:encryption_of_one}
E(1) = E(M_1 x + M_2 y) = x E(M_1) + y E(M_2) = x C_1 + y C_2.

By multiplying $E(1)$ by $m$, EveCorp has access to a plaintext and encryption of $m$ for any message $m$.

Now suppose that EveCorp can always recognize an encryption of $0$. Then EveCorp can mount a variety of attacks exposing information about the data it holds.

For example, EveCorp can test whether a particular message $m$ is contained in the encrypted dataset. First, EveCorp generates a ciphertext $C_m$ for $m$ by multiplying $E(1)$ by $m$, as in \eqref{eq:encryption_of_one}. Then for each ciphertext $C$ in the dataset, EveCorp computes $C – C_m$. If $m$ is contained in the dataset, then $C – C_m$ will be an encryption of $0$ for the $C$ corresponding to $m$. EveCorp recognizes this, and now knows that $m$ is in the data. To be more specific, perhaps a list of encrypted names of medical patients appears in the data, and EveCorp wants to see if JohnDoe is in that list. If they can recognize encryptions of $0$, then EveCorp can access this information.

And thus it is unacceptable for external entities to be able to consistently recognize encryptions of $0$.

Up to now, I’ve been a bit loose by saying “an encryption of zero” or “an encryption of $m$”. The reason for this is that to protect against recognition of encryptions of $0$, some entropy is added to the encryption function $E(\cdot)$, making it multivalued. So if we have a message $M$ and we encrypt it once to get $E(M)$, and we encrypt $M$ later and get $E'(M)$, it is often not true that $E(M) = E'(M)$, even though they are both encryptions of the same message. But these systems are designed so that it is true that $C(E(M)) = C(E'(M)) = M$, so that the entropy doesn’t matter.

This is a separate matter, and something that I will probably return to later.

## A Brief Notebook on Cryptography

This is a post written for my Spring 2016 Number Theory class at Brown University. It was written and originally presented from a Jupyter Notebook, and changed for presentation on this site. There is a version of the notebook available at github.

# Cryptography¶

Recall the basic setup of cryptography. We have two people, Anabel and Bartolo. Anabel wants to send Bartolo a secure message. What do we mean by “secure?” We mean that even though that dastardly Eve might intercept and read the transmitted message, Eve won’t learn anything about the actual message Anabel wants to send to Bartolo.

Before the 1970s, the only way for Anabel to send Bartolo a secure message required Anabel and Bartolo to get together beforehand and agree on a secret method of communicating. To communicate, Anabel first decides on a message. The original message is sometimes called the plaintext. She then encrypts the message, producing a ciphertext.

She then sends the ciphertext. If Eve gets hold of hte ciphertext, she shouldn’t be able to decode it (unless it’s a poor encryption scheme).

When Bartolo receives the ciphertext, he can decrypt it to recover the plaintext message, since he agreed on the encryption scheme beforehand.

## Caesar Shift¶

The first known instance of cryptography came from Julius Caesar. It was not a very good method. To encrypt a message, he shifted each letter over by 2, so for instance “A” becomes “C”, and “B” becomes “D”, and so on.

Let’s see what sort of message comes out.

alpha = "abcdefghijklmnopqrstuvwxyz".upper()
punct = ",.?:;'\n "

from functools import partial

def shift(l, s=2):
l = l.upper()
return alpha[(alpha.index(l) + s) % 26]

def caesar_shift_encrypt(m, s=2):
m = m.upper()
c = "".join(map(partial(shift, s=s), m))
return c

def caesar_shift_decrypt(c, s=-2):
c = c.upper()
m = "".join(map(partial(shift, s=s), c))
return m

print "Original Message: HI"
print "Ciphertext:", caesar_shift_encrypt("hi")

Original Message: HI
Ciphertext: JK

m = """To be, or not to be, that is the question:
Whether 'tis Nobler in the mind to suffer
The Slings and Arrows of outrageous Fortune,
Or to take Arms against a Sea of troubles,
And by opposing end them."""

m = "".join([l for l in m if not l in punct])

print "Original Message:"
print m

print
print "Ciphertext:"
tobe_ciphertext = caesar_shift_encrypt(m)
print tobe_ciphertext

Original Message:
TobeornottobethatisthequestionWhethertisNoblerinthemindtosuffer
TheSlingsandArrowsofoutrageousFortuneOrtotakeArmsagainstaSeaof
troublesAndbyopposingendthem

Ciphertext:
VQDGQTPQVVQDGVJCVKUVJGSWGUVKQPYJGVJGTVKUPQDNGTKPVJGOKPFVQUWHHGT
VJGUNKPIUCPFCTTQYUQHQWVTCIGQWUHQTVWPGQTVQVCMGCTOUCICKPUVCUGCQH
VTQWDNGUCPFDAQRRQUKPIGPFVJGO

print "Decrypted first message:", caesar_shift_decrypt("JK")

Decrypted first message: HI

print "Decrypted second message:"
print caesar_shift_decrypt(tobe_ciphertext)

Decrypted second message:
TOBEORNOTTOBETHATISTHEQUESTIONWHETHERTISNOBLERINTHEMINDTOSUFFER
THESLINGSANDARROWSOFOUTRAGEOUSFORTUNEORTOTAKEARMSAGAINSTASEAOF
TROUBLESANDBYOPPOSINGENDTHEM


Is this a good encryption scheme? No, not really. There are only 26 different things to try. This can be decrypted very quickly and easily, even if entirely by hand.

## Substitution Cipher¶

A slightly different scheme is to choose a different letter for each letter. For instance, maybe “A” actually means “G” while “B” actually means “E”. We call this a substitution cipher as each letter is substituted for another.

import random
permutation = list(alpha)
random.shuffle(permutation)

# Display the new alphabet
print alpha
subs = "".join(permutation)
print subs

ABCDEFGHIJKLMNOPQRSTUVWXYZ
EMJSLZKAYDGTWCHBXORVPNUQIF

def subs_cipher_encrypt(m):
m = "".join([l.upper() for l in m if not l in punct])
return "".join([subs[alpha.find(l)] for l in m])

def subs_cipher_decrypt(c):
c = "".join([l.upper() for l in c if not l in punct])
return "".join([alpha[subs.find(l)] for l in c])

m1 = "this is a test"

print "Original message:", m1
c1 = subs_cipher_encrypt(m1)

print
print "Encrypted Message:", c1

print
print "Decrypted Message:", subs_cipher_decrypt(c1)

Original message: this is a test

Encrypted Message: VAYRYREVLRV

Decrypted Message: THISISATEST

print "Original message:"
print m

print
c2 = subs_cipher_encrypt(m)
print "Encrypted Message:"
print c2

print
print "Decrypted Message:"
print subs_cipher_decrypt(c2)

Original message:
TobeornottobethatisthequestionWhethertisNoblerinthemindtosuffer
TheSlingsandArrowsofoutrageousFortuneOrtotakeArmsagainstaSeaof
troublesAndbyopposingendthem

Encrypted Message:
VHMLHOCHVVHMLVAEVYRVALXPLRVYHCUALVALOVYRCHMTLOYCVALWYCSVHRPZZLO
VALRTYCKRECSEOOHURHZHPVOEKLHPRZHOVPCLHOVHVEGLEOWREKEYCRVERLEHZ
VOHPMTLRECSMIHBBHRYCKLCSVALW

Decrypted Message:
TOBEORNOTTOBETHATISTHEQUESTIONWHETHERTISNOBLERINTHEMINDTOSUFFER
THESLINGSANDARROWSOFOUTRAGEOUSFORTUNEORTOTAKEARMSAGAINSTASEAOF
TROUBLESANDBYOPPOSINGENDTHEM


Is this a good encryption scheme? Still no. In fact, these are routinely used as puzzles in newspapers or puzzle books, because given a reasonably long message, it’s pretty easy to figure it out using things like the frequency of letters.

For instance, in English, the letters RSTLNEAO are very common, and much more common than other letters. So one might start to guess that the most common letter in the ciphertext is one of these. More powerfully, one might try to see which pairs of letters (called bigrams) are common, and look for those in the ciphertext, and so on.

From this sort of thought process, encryption schemes that ultimately rely on a single secret alphabet (even if it’s not our typical alphabet) fall pretty quickly. So… what about polyalphabetic ciphers? For instance, what if each group of 5 letters uses a different set of alphabets?

This is a great avenue for exploration, and there are lots of such encryption schemes that we won’t discuss in this class. But a class on cryptography (or a book on cryptography) would certainly go into some of these. It might also be a reasonable avenue of exploration for a final project.

## The German Enigma¶

One very well-known polyalphabetic encryption scheme is the German Enigma used before and during World War II. This was by far the most complicated cryptosystem in use up to that point, and the story of how it was broken is a long and tricky one. Intial successes towards breaking the Enigma came through the work of Polish mathematicians, fearful (and rightfully so) of the Germans across the border. By 1937, they had built replicas and understood many details of the Enigma system. But in 1938, the Germans shifted to a more secure and complicated cryptosystem. Just weeks before the German invasion of Poland and the beginning of World War II, Polish mathematicians sent their work and notes to mathematicians in France and Britain, who carried out this work.

The second major progress towards breaking the Enigma occurred largely in Bletchley Park in Britain, a communication center with an enormous dedicated effort to breaking the Enigma. This is where the tragic tale of Alan Turing, recently popularized through the movie The Imitation Game, begins. This is also the origin tale for modern computers, as Alan Turing developed electromechanical computers to help break the Enigma.

The Enigma worked by having a series of cogs or rotors whose positions determined a substitution cipher. After each letter, the positions were changed through a mechanical process. An Enigma machine is a very impressive machine to look at [and the “computer” Alan Turing used to help break them was also very impressive].

Below, I have implemented an Enigma, by default set to 4 rotors. I don’t expect one to understand the implementation. The interesting part is how meaningless the output message looks. Note that I’ve kept the spacing and punctuation from the original message for easier comparison. Really, you wouldn’t do this.

The plaintext used for demonstration is from Wikipedia’s article on the Enigma.

from random import shuffle,randint,choice
from copy import copy
num_alphabet = range(26)

def en_shift(l, n):                         # Rotate cogs and arrays
return l[n:] + l[:n]

class Cog:                                  # Each cog has a substitution cipher
def __init__(self):
self.shuf = copy(num_alphabet)
shuffle(self.shuf)                  # Create the individual substition cipher
return                              # Really, these were not random

def subs_in(self,i):                    # Perform a substition
return self.shuf[i]

def subs_out(self,i):                   # Perform a reverse substition
return self.shuf.index(i)

def rotate(self):                       # Rotate the cog by 1.
self.shuf = en_shift(self.shuf, 1)

def setcog(self,a):                     # Set up a particular substitution
self.shuf = a

class Enigma:
self.numcogs = numcogs
self.cogs = []
self.oCogs = []                     # "Original Cog positions"

for i in range(0,self.numcogs):     # Create the cogs
self.cogs.append(Cog())
self.oCogs.append(self.cogs[i].shuf)

refabet = copy(num_alphabet)
self.reflector = copy(num_alphabet)
while len(refabet) > 0:             # Pair letters in the reflector
a = choice(refabet)
refabet.remove(a)

b = choice(refabet)
refabet.remove(b)

self.reflector[a] = b
self.reflector[b] = a

def print_setup(self): # Print out substituion setup.
print "Enigma Setup:\nCogs: ",self.numcogs,"\nCog arrangement:"
for i in range(0,self.numcogs):
print self.cogs[i].shuf
print "Reflector arrangement:\n",self.reflector,"\n"

def reset(self):
for i in range(0,self.numcogs):
self.cogs[i].setcog(self.oCogs[i])

def encode(self,text):
t = 0     # Ticker counter
ciphertext=""
for l in text.lower():
num = ord(l) % 97
# Handle special characters for readability
if (num>25 or num<0):
ciphertext += l
else:
pass

else:
# Pass through cogs, reflect, then return through cogs
t += 1
for i in range(self.numcogs):
num = self.cogs[i].subs_in(num)

num = self.reflector[num]

for i in range(self.numcogs):
num = self.cogs[self.numcogs-i-1].subs_out(num)
ciphertext += "" + chr(97+num)

# Rotate cogs
for i in range(self.numcogs):
if ( t % ((i*6)+1) == 0 ):
self.cogs[i].rotate()
return ciphertext.upper()

plaintext="""When placed in an Enigma, each rotor can be set to one of 26 possible positions.
When inserted, it can be turned by hand using the grooved finger-wheel, which protrudes from
the internal Enigma cover when closed. So that the operator can know the rotor's position,
each had an alphabet tyre (or letter ring) attached to the outside of the rotor disk, with
26 characters (typically letters); one of these could be seen through the window, thus indicating
the rotational position of the rotor. In early models, the alphabet ring was fixed to the rotor
disk. A later improvement was the ability to adjust the alphabet ring relative to the rotor disk.
The position of the ring was known as the Ringstellung ("ring setting"), and was a part of the
initial setting prior to an operating session. In modern terms it was a part of the
initialization vector."""

# Remove newlines for encryption
pt = "".join([l.upper() for l in plaintext if not l == "\n"])
# pt = "".join([l.upper() for l in plaintext if not l in punct])

x=enigma(4)
#x.print_setup()

print "Original Message:"
print pt

print
ciphertext = x.encode(pt)
print "Encrypted Message"
print ciphertext

print
# Decryption and encryption are symmetric, so to decode we reset and re-encrypt.
x.reset()
decipheredtext = x.encode(ciphertext)
print "Decrypted Message:"
print decipheredtext

Original Message:
WHEN PLACED IN AN ENIGMA, EACH ROTOR CAN BE SET TO ONE OF 26 POSSIBLE POSITIONS.
WHEN INSERTED, IT CAN BE TURNED BY HAND USING THE GROOVED FINGER-WHEEL, WHICH
PROTRUDES FROM THE INTERNAL ENIGMA COVER WHEN CLOSED. SO THAT THE OPERATOR CAN
KNOW THE ROTOR'S POSITION, EACH HAD AN ALPHABET TYRE (OR LETTER RING) ATTACHED
TO THE OUTSIDE OF THE ROTOR DISK, WITH 26 CHARACTERS (TYPICALLY LETTERS); ONE
OF THESE COULD BE SEEN THROUGH THE WINDOW, THUS INDICATING THE ROTATIONAL
POSITION OF THE ROTOR. IN EARLY MODELS, THE ALPHABET RING WAS FIXED TO THE
ROTOR DISK. A LATER IMPROVEMENT WAS THE ABILITY TO ADJUST THE ALPHABET RING
RELATIVE TO THE ROTOR DISK. THE POSITION OF THE RING WAS KNOWN AS THE
RINGSTELLUNG ("RING SETTING"), AND WAS A PART OF THE INITIAL SETTING PRIOR TO
AN OPERATING SESSION. IN MODERN TERMS IT WAS A PART OF THE INITIALIZATION VECTOR.

Encrypted Message
RYQY LWMVIH OH EK GGWFBO, PDZN FNALL ZEP AP IUD JM FEV UG 26 HGRKODYT XWOLIYTSA.
TJQK KBCNVMHG, VS YBB XI BYZTVU XP BGWP IFNIY UQZ IQUPTKJ QXPVYE-EAOVT, PYMNN
RXFIOWYVK LWNN OJL JXZBTRVP YMQCXX STJQF KXNZ LXSWDC. LK KGRH ZKT RDZUMCWA GKY
LYKC LLV ZNFAD'Z BAXLDFTH, QSHA NBY RZ SXEXDAMP FXUS (WC RWHZOX ARWP) VKYNDJQP
WL OLW ICBALPI RV ISD GXSDQ ZKMJ, OHNN 26 IBGUHNYIQE (GDBNTEBWP QGECUNA); QPG
SQ JYUQX NDBWB NM AAUF RUNKYDL OLD LMPZQV, ZMKP SQZFDEHKCI KLJ AKPZLRAZZX
QXPJEXLV HS AFG IIMGK. NT PVAYP RFOKYV, XUY CHZAQEXG DUSS CKN YENSR FX PLS CYMZK
YHTB. J RLZLB DNIDWNWAIOO HOK PGM HVXXHIJ VV SCTNIC QGM TFKPQYPQ XFQU PSAECHTX
RA QUW CUNRV JCUW. LCP GFKZLLXW LN YWF OAQM CMF YVTQH EI JAU LZTXEYDGDFLQ
("CDBS TQHUEIR"), BLN QIG O UHJJ DV DQY KQGVFKI EBEFRCT WEZWG LJ WC NIAUKIYQJ
EHZLZLS. TY XPZSBL IPGWN ZS IUY E YQMD BW NVF QYWSDMTRSNSHYO GJGNUL.

Decrypted Message:
WHEN PLACED IN AN ENIGMA, EACH ROTOR CAN BE SET TO ONE OF 26 POSSIBLE POSITIONS.
WHEN INSERTED, IT CAN BE TURNED BY HAND USING THE GROOVED FINGER-WHEEL, WHICH
PROTRUDES FROM THE INTERNAL ENIGMA COVER WHEN CLOSED. SO THAT THE OPERATOR
CAN KNOW THE ROTOR'S POSITION, EACH HAD AN ALPHABET TYRE (OR LETTER RING)
ATTACHED TO THE OUTSIDE OF THE ROTOR DISK, WITH 26 CHARACTERS (TYPICALLY
LETTERS); ONE OF THESE COULD BE SEEN THROUGH THE WINDOW, THUS INDICATING THE
ROTATIONAL POSITION OF THE ROTOR. IN EARLY MODELS, THE ALPHABET RING WAS FIXED
TO THE ROTOR DISK. A LATER IMPROVEMENT WAS THE ABILITY TO ADJUST THE ALPHABET
RING RELATIVE TO THE ROTOR DISK. THE POSITION OF THE RING WAS KNOWN AS THE
RINGSTELLUNG ("RING SETTING"), AND WAS A PART OF THE INITIAL SETTING PRIOR TO
AN OPERATING SESSION. IN MODERN TERMS IT WAS A PART OF THE INITIALIZATION VECTOR.


The advent of computers brought in a paradigm shift in approaches towards cryptography. Prior to computers, one of the ways of maintaining security was to come up with a hidden key and a hidden cryptosystem, and keep it safe merely by not letting anyone know anything about how it actually worked at all. This has the short cute name security through obscurity. As the number of type of attacks on cryptosystems are much, much higher with computers, a different model of security and safety became necessary.

It is interesting to note that it is not obvious that security through obscurity is always bad, as long as it’s really really well-hidden. This is relevant to some problems concerning current events and cryptography.

# Public Key Cryptography¶

The new model begins with a slightly different setup. We should think of Anabel and Bartolo as sitting on opposite sides of a classroom, trying to communicate securely even though there are lots of people in the middle of the classroom who might be listening in. In particular, Anabel has something she wants to tell Bartolo.

Instead of keeping the cryptosystem secret, Bartolo tells everyone (in our metaphor, he shouts to the entire classroom) a public key K, and explains how to use it to send him a message. Anabel uses this key to encrypt her message. She then sends this message to Bartolo.

If the system is well-designed, no one will be able to understand the ciphertext even though they all know how the cryptosystem works. This is why the system is called Public Key.

Bartolo receives this message and (using something only he knows) he decrypts the message.

We will learn one such cryptosystem here: RSA, named after Rivest, Shamir, and Addleman — the first major public key cryptosystem.

## RSA¶

Bartolo takes two primes such as $p = 12553$ and $q = 13007$. He notes their product
$$m = pq = 163276871$$
and computes $\varphi(m)$,
$$\varphi(m) = (p-1)(q-1) = 163251312.$$
Finally, he chooses some integer $k$ relatively prime to $\varphi(m)$, like say
$$k = 79921.$$
Then the public key he distributes is
$$(m, k) = (163276871, 79921).$$

In order to send Bartolo a message using this key, Anabel must convert her message to numbers. She might use the identification A = 11, B = 12, C = 13, … and concatenate her numbers. To send the word CAB, for instance, she would send 131112. Let’s say that Anabel wants to send the message

NUMBER THEORY IS THE QUEEN OF THE SCIENCES

Then she needs to convert this to numbers.

conversion_dict = dict()
alpha = "abcdefghijklmnopqrstuvwxyz".upper()
curnum = 11
for l in alpha:
conversion_dict[l] = curnum
curnum += 1

print "Original Message:"
msg = "NUMBERTHEORYISTHEQUEENOFTHESCIENCES"
print msg
print

def letters_to_numbers(m):
return "".join([str(conversion_dict[l]) for l in m.upper()])

print "Numerical Message:"
msg_num = letters_to_numbers(msg)
print msg_num

Original Message:
NUMBERTHEORYISTHEQUEENOFTHESCIENCES

Numerical Message:
2431231215283018152528351929301815273115152425163018152913191524131529


So Anabel’s message is the number

$$2431231215283018152528351929301815273115152425163018152913191524131529$$

which she wants to encrypt and send to Bartolo. To make this manageable, she cuts the message into 8-digit numbers,

$$24312312, 15283018, 15252835, 19293018, 15273115, 15242516, 30181529, 13191524, 131529.$$

To send her message, she takes one of the 8-digit blocks and raises it to the power of $k$ modulo $m$. That is, to transmit the first block, she computes

$$24312312^{79921} \equiv 13851252 \pmod{163276871}.$$

# Secret information
p = 12553
q = 13007
phi = (p-1)*(q-1) # varphi(pq)

# Public information
m = p*q # 163276871
k = 79921

print pow(24312312, k, m)

13851252


She sends this number
$$13851252$$
to Bartolo (maybe by shouting. Even though everyone can hear, they cannot decrypt it). How does Bartolo decrypt this message?

He computes $\varphi(m) = (p-1)(q-1)$ (which he can do because he knows $p$ and $q$ separately), and then finds a solution $u$ to
$$uk = 1 + \varphi(m) v.$$
This can be done quickly through the Euclidean Algorithm.

def extended_euclidean(a,b):
if b == 0:
return (1,0,a)
else :
x, y, gcd = extended_euclidean(b, a % b) # Aside: Python has no tail recursion
return y, x - y * (a // b),gcd           # But it does have meaningful stack traces

# This version comes from Exercise 6.3 in the book, but without recursion
def extended_euclidean2(a,b):
x = 1
g = a
v = 0
w = b
while w != 0:
q = g // w
t = g - q*w
s = x - q*v
x,g = v,w
v,w = s,t
y = (g - a*x) / b
return (x,y,g)

def modular_inverse(a,m) :
x,y,gcd = extended_euclidean(a,m)
if gcd == 1 :
return x % m
else :
return None

print "k, p, q:", k, p, q
print
u = modular_inverse(k,(p-1)*(q-1))
print u

k, p, q: 79921 12553 13007

145604785


In particular, Bartolo computes that his $u = 145604785$. To recover the message, he takes the number $13851252$ sent to him by Anabel and raises it to the $u$ power. He computes
$$13851252^{u} \equiv 24312312 \pmod{pq},$$
which we can see must be true as
$$13851252^{u} \equiv (24312312^{k})^u \equiv 24312312^{1 + \varphi(pq)v} \equiv 24312312 \pmod{pq}.$$
In this last step, we have used Euler’s Theorem to see that
$$24312312^{\varphi(pq)v} \equiv 1 \pmod{pq}.$$

# Checking this power explicitly.
print pow(13851252, 145604785, m)

24312312


Now Bartolo needs to perform this process for each 8-digit chunk that Anabel sent over. Note that the work is very easy, as he computes the integer $u$ only once. Each other time, he simply computes $c^u \pmod m$ for each ciphertext $c$, and this is very fast with repeated-squaring.

We do this below, in an automated fashion, step by step.

First, we split the message into 8-digit chunks.

# Break into chunks of 8 digits in length.
def chunk8(message_number):
cp = str(message_number)
ret_list = []
while len(cp) > 7:
ret_list.append(cp[:8])
cp = cp[8:]
if cp:
ret_list.append(cp)
return ret_list

msg_list = chunk8(msg_num)
print msg_list

['24312312', '15283018', '15252835', '19293018', '15273115',
'15242516', '30181529', '13191524', '131529']


This is a numeric representation of the message Anabel wants to send Bartolo. So she encrypts each chunk. This is done below

# Compute ciphertexts separately on each 8-digit chunk.
def encrypt_chunks(chunked_list):
ret_list = []
for chunk in chunked_list:
#print chunk
#print int(chunk)
ret_list.append(pow(int(chunk), k, m))
return ret_list

cipher_list = encrypt_chunks(msg_list)
print cipher_list

[13851252, 14944940, 158577269, 117640431, 139757098, 25099917,
88562046, 6640362, 10543199]


This is the encrypted message. Having computed this, Anabel sends this message to Bartolo.

To decrypt the message, Bartolo uses his knowledge of $u$, which comes from his ability to compute $\varphi(pq)$, and decrypts each part of the message. This looks like below.

# Decipher the ciphertexts all in the same way
def decrypt_chunks(chunked_list):
ret_list = []
for chunk in chunked_list:
ret_list.append(pow(int(chunk), u, m))
return ret_list

decipher_list = decrypt_chunks(cipher_list)
print decipher_list

[24312312, 15283018, 15252835, 19293018, 15273115, 15242516,
30181529, 13191524, 131529]


Finally, Bartolo concatenates these numbers together and translates them back into letters. Will he get the right message back?

alpha = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# Collect deciphered texts into a single list, and translate back into letters.
def chunks_to_letters(chunked_list):
s = "".join([str(chunk) for chunk in chunked_list])
ret_str = ""
while s:
ret_str += alpha[int(s[:2])-11].upper()
s = s[2:]
return ret_str

print chunks_to_letters(decipher_list)

NUMBERTHEORYISTHEQUEENOFTHESCIENCES


Yes! Bartolo successfully decrypts the message and sees that Anabel thinks that Number Theory is the Queen of the Sciences. This is a quote from Gauss, the famous mathematician who has been popping up again and again in this course.

## Why is this Secure?¶

Let’s pause to think about why this is secure.

What if someone catches the message in transit? Suppose Eve is eavesdropping and hears Anabel’s first chunk, $13851252$. How would she decrypt it?

Eve knows that she wants to solve
$$x^k \equiv 13851252 \pmod {pq}$$
or rather
$$x^{79921} \equiv 13851252 \pmod {163276871}.$$
How can she do that? We can do that because we know to raise this to a particular power depending on $\varphi(163276871)$. But Eve doesn’t know what $\varphi(163276871)$ is since she can’t factor $163276871$. In fact, knowing $\varphi(163276871)$ is exactly as hard as factoring $163276871$.

But if Eve could somehow find $79921$st roots modulo $163276871$, or if Eve could factor $163276871$, then she would be able to decrypt the message. These are both really hard problems! And it’s these problems that give the method its security.

More generally, one might use primes $p$ and $q$ that are each about $200$ digits long, and a fairly large $k$. Then the resulting $m$ would be about $400$ digits, which is far larger than we know how to factor effectively. The reason for choosing a somewhat large $k$ is for security reasons that are beyond the scope of this segment. The relevant idea here is that since this is a publicly known encryption scheme, many people have strenghtened it over the years by making it more secure against every clever attack known. This is another, sometimes overlooked benefit of public-key cryptography: since the methodology isn’t secret, everyone can contribute to its security — indeed, it is in the interest of anyone desiring such security to contribute. This is sort of the exact opposite of Security through Obscurity.

The nature of code being open, public, private, or secret is also very topical in current events. Recently, Volkswagen cheated in its car emissions-software and had it report false outputs, leading to a large scandal. Their software is proprietary and secret, and the deliberate bug went unnoticed for years. Some argue that this means that more software, and especially software that either serves the public or that is under strong jurisdiction, should be publicly viewable for analysis.

Another relevant current case is that the code for most voting machines in the United States is proprietary and secret. Hopefully they aren’t cheating!

On the other side, many say that it is necessary for companies to be able to have secret software for at least some time period to recuperate the expenses that go into developing the software. This is similar to how drug companies have patents on new drugs for a number of years. This way, a new successful drug pays for its development since the company can charge above the otherwise-market-rate.

Further, many say that opening some code would open it up to attacks from malicious users who otherwise wouldn’t be able to see the code. Of course, this sounds a lot like trying for security through obscurity.

This is a very relevant and big topic, and the shape it takes over the next few years may very well have long-lasting impacts.

## Condensed Version¶

Now that we’ve gone through it all once, we have a condensed RSA system set up with our $p, q,$ and $k$ from above. To show that this can be done quickly with a computer, let’s do another right now.

Let us encrypt, transmit, and decrypt the message

“I have never done anything useful. No discovery of mine has made, or is likely to make, directly or indirectly, for good or ill, the least difference to the amenity of the world”.

First, we prepare the message for conversion to numbers.

message = """I have never done anything useful. No discovery of mine has made,
or is likely to make, directly or indirectly, for good or ill, the least
difference to the amenity of the world"""

message = "".join([l.upper() for l in message if not l in "\n .,"])
print "Our message:\n"+message

Our message:
TOMAKEDIRECTLYORINDIRECTLYFORGOODORILLTHELEASTDIFFERENCETOTHE
AMENITYOFTHEWORLD


Now we convert the message to numbers, and transform those numbers into 8-digit chunks.

numerical_message = letters_to_numbers(message)
print "Our message, converted to numbers:"
print numerical_message
print

plaintext_chunks = chunk8(numerical_message)
print "Separated into 8-digit chunks:"
print plaintext_chunks

Our message, converted to numbers:
1918113215241532152814252415112435301819241731291516312224251419
2913253215283525162319241518112923111415252819292219211522353025
2311211514192815133022352528192414192815133022351625281725251425
2819222230181522151129301419161615281524131530253018151123152419
303525163018153325282214

Separated into 8-digit chunks:
['19181132', '15241532', '15281425', '24151124', '35301819',
'24173129', '15163122', '24251419', '29132532', '15283525',
'16231924', '15181129', '23111415', '25281929', '22192115',
'22353025', '23112115', '14192815', '13302235', '25281924',
'14192815', '13302235', '16252817', '25251425', '28192222',
'30181522', '15112930', '14191616', '15281524', '13153025',
'30181511', '23152419', '30352516', '30181533', '25282214']


We encrypt each chunk by computing $P^k \bmod {m}$ for each plaintext chunk $P$.

ciphertext_chunks = encrypt_chunks(plaintext_chunks)
print ciphertext_chunks

[99080958, 142898385, 80369161, 11935375, 108220081, 82708130,
158605094, 96274325, 154177847, 121856444, 91409978, 47916550,
155466420, 92033719, 95710042, 86490776, 15468891, 139085799,
68027514, 53153945, 139085799, 68027514, 9216376, 155619290,
83776861, 132272900, 57738842, 119368739, 88984801, 83144549,
136916742, 13608445, 92485089, 89508242, 25375188]


This is the message that Anabel can sent Bartolo.

To decrypt it, he computes $c^u \bmod m$ for each ciphertext chunk $c$.

deciphered_chunks = decrypt_chunks(ciphertext_chunks)
print "Deciphered chunks:"
print deciphered_chunks

Deciphered chunks:
[19181132, 15241532, 15281425, 24151124, 35301819, 24173129,
15163122, 24251419, 29132532, 15283525, 16231924, 15181129,
23111415, 25281929, 22192115, 22353025, 23112115, 14192815,
13302235, 25281924, 14192815, 13302235, 16252817, 25251425,
28192222, 30181522, 15112930, 14191616, 15281524, 13153025,
30181511, 23152419, 30352516, 30181533, 25282214]


Finally, he translates the chunks back into letters.

decoded_message = chunks_to_letters(deciphered_chunks)
print "Decoded Message:"
print decoded_message

Decoded Message:
TOMAKEDIRECTLYORINDIRECTLYFORGOODORILLTHELEASTDIFFERENCETOTHE
AMENITYOFTHEWORLD


Even with large numbers, RSA is pretty fast. But one of the key things that one can do with RSA is securely transmit secret keys for other types of faster-encryption that don’t work in a public-key sense. There is a lot of material in this subject, and it’s very important.

The study of sending and receiving secret messages is called Cryptography. There are lots of interesting related topics, some of which we’ll touch on in class.

The study of analyzing and breaking cryptosystems is called Cryptanalysis, and is something I find quite fun. But it’s also quite intense.

I should mention that in practice, RSA is performed a little bit differently to make it more secure.

Aside: this is the first time I’ve converted a Jupyter Notebook, and it turns out it’s very easy. However, there are a few formatting annoyances that I didn’t consider, but which are too minor to spend time fixing. But now I know!
| Tagged , , , , | 4 Comments

## Twenty Mathematicians, Two Hard Problems, One Week, IdeaLab2013

July has been an exciting and busy month for me. I taught number theory 3 hours a day, 5 days a week, for 3 weeks to (mostly) devoted and motivated high school students in the Summer@Brown program. In the middle, I moved to Massachusetts. Immediately after the Summer@Brown program ended, I was given the opportunity to return to ICERM to participate in an experimental program called an IdeaLab.

IdeaLab invited 20 early career mathematicians to come together for a week and to generate ideas on two very different problems: Tipping Points in Climate Systems and Efficient Fully Homomorphic Encryption. Although I plan on writing a bit more about each of these problems and the IdeaLab process in action (at least from my point of view), I should say something about what these are.

Models of Earth’s climate are used all the time, to give daily weather reports, to predict and warn about hurricanes, to attempt to understand the effects of anthropogenic sources of carbon on long-term climate. As we know from uncertainty about weather reports, these models aren’t perfect. In particular, they don’t currently predict sudden, abrupt changes called ‘Tippling points.’ But are tipping points possible? There have been warm periods following ice-ages in the past, so it seems that there might be tipping points that aren’t modelled in the system. Understanding these form the basis for the idea behind the Tipping Points in Climate Systems project. This project also forms another link in Mathematics of Planet Earth.

On the other hand, homomorphic encryption is a topic in modern cryptography. To encrypt a message is to make it hard or impossible for others to read it unless they have a ‘key.’ You might think that you wouldn’t want someone holding onto an encrypted data to be able to do anything with the data, and in most modern encryption algorithms this is the case. But what if we were able to give Google an encrypted dataset and ask them to perform a search on it? Is it possible to have a secure encryption that would allow Google to do some sort of search algorithm and give us the results, but without Google ever understanding the data itself? It may seem far-fetched, but this is exactly the idea behind the Efficient Fully Homomorphic Encryption group. Surprisingly enough, it is possible. But known methods are obnoxiously slow and infeasible. This is why the group was after ‘efficient’ encryption.

So 20 early career mathematicians from all sorts of areas of mathematics gathered to think about these two questions. For the rest of this post, I’d like to talk about the structure and my thoughts on the IdeaLab process. In later posts, I’ll talk about each of the two major topics and what sorts of ideas came out of the process.

## Chinese Remainder Theorem (SummerNT)

This post picks up from the previous post on Summer@Brown number theory from 2013.

Now that we’d established ideas about solving the modular equation $ax \equiv c \mod m$, solving the linear diophantine equation $ax + by = c$, and about general modular arithmetic, we began to explore systems of modular equations. That is, we began to look at equations like

Suppose $x$ satisfies the following three modular equations (rather, the following system of linear congruences):

$x \equiv 1 \mod 5$

$x \equiv 2 \mod 7$

$x \equiv 3 \mod 9$

Can we find out what $x$ is? This is a clear parallel to solving systems of linear equations, as is usually done in algebra I or II in secondary school. A common way to solve systems of linear equations is to solve for a variable and substitute it into the next equation. We can do something similar here.

From the first equation, we know that $x = 1 + 5a$ for some $a$. Substituting this into the second equation, we get that $1 + 5a \equiv 2 \mod 7$, or that $5a \equiv 1 \mod 7$. So $a$ will be the modular inverse of $5 \mod 7$. A quick calculation (or a slightly less quick Euclidean algorithm in the general case) shows that the inverse is $3$. Multiplying both sides by $3$ yields $a \equiv 3 \mod 7$, or rather that $a = 3 + 7b$ for some $b$. Back substituting, we see that this means that $x = 1+5a = 1+5(3+7b)$, or that $x = 16 + 35b$.

Now we repeat this work, using the third equation. $16 + 35b \equiv 3 \mod 9$, so that $8b \equiv 5 \mod 9$. Another quick calculation (or Euclidean algorithm) shows that this means $b \equiv 4 \mod 9$, or rather $b = 4 + 9c$ for some $c$. Putting this back into $x$ yields the final answer:

$x = 16 + 35(4 + 9c) = 156 + 315c$

$x \equiv 156 \mod 315$

And if you go back and check, you can see that this works. $\diamondsuit$

There is another, very slick, method as well. This was a clever solution mentioned in class. The idea is to construct a solution directly. The way we’re going to do this is to set up a sum, where each part only contributes to one of the three modular equations. In particular, note that if we take something like $7 \cdot 9 \cdot [7\cdot9]_5^{-1}$, where this inverse means the modular inverse with respect to $5$, then this vanishes mod $7$ and mod $9$, but gives $1 \mod 5$. Similarly $2\cdot 5 \cdot 9 \cdot [5\cdot9]_7^{-1}$ vanishes mod 5 and mod 9 but leaves the right remainder mod 2, and $5 \cdot 7 \cdot [5\cdot 7]_9^{-1}$ vanishes mod 5 and mod 7, but leaves the right remainder mod 9.

Summing them together yields a solution (Do you see why?). The really nice thing about this algorithm to get the solution is that is parallelizes really well, meaning that you can give different computers separate problems, and then combine the things together to get the final answer. This is going to come up again later in this post.

These are two solutions that follow along the idea of the Chinese Remainder Theorem (CRT), which in general says that as long as the moduli are relative prime, then the system

$a_1 x \equiv b_1 \mod m_1$

$a_2 x \equiv b_2 \mod m_2$

$\cdots$

$a_k x \equiv b_k \mod m_k$

will always have a unique solution $\mod m_1m_2 \ldots m_k$. Note, this is two statements: there is a solution (statement 1), and the statement is unique up to modding by the product of this moduli (statement 2). Proof Sketch: Either of the two methods described above to solve that problem can lead to a proof here. But there is one big step that makes such a proof much easier. Once you’ve shown that the CRT is true for a system of two congruences (effectively meaning you can replace them by one congruence), this means that you can use induction. You can reduce the n+1st case to the nth case using your newfound knowledge of how to combine two equations into one. Then the inductive hypothesis carries out the proof.

Note also that it’s pretty easy to go backwards. If I know that $x \equiv 12 \mod 30$, then I know that $x$ will also be the solution to the system

$x \equiv 2 \mod 5$

$x \equiv 0 \mod 6$

In fact, a higher view of the CRT reveals that the great strength is that considering a number mod a set of relatively prime moduli is the exact same (isomorphic to) considering a number mod the product of the moduli.

The remainder of this post will be about why the CRT is cool and useful.

#### Application 1: Multiplying Large Numbers

Firstly, the easier application. Suppose you have two really large integers $a,b$ (by really large, I mean with tens or hundreds of digits at least – for concreteness, say they each have $n$ digits). When a computer computes their product $ab$, it has to perform $n^2$ digit multiplications, which can be a whole lot if $n$ is big. But a computer can calculate mods of numbers in something like $\log n$ time, which is much much much faster. So one way to quickly compute the product of two really large numbers is to use the Chinese Remainder Theorem to represent each of $a$ and $b$ with a set of much smaller congruences. For example (though we’ll be using small numbers), say we want to multiply $12$ by $21$. We might represent $12$ by $12 \equiv 2 \mod 5, 5 \mod 7, 1 \mod 11$ and represent $21$ by $21 \equiv 1 \mod 5, 0 \mod 7, 10 \mod 11$. To find their product, calculate their product in each of the moduli: $2 \cdot 1 \equiv 2 \mod 5, 5 \cdot 0 \equiv 0 \mod 7, 1 \cdot 10 \equiv 10 \mod 11$. We know we can get a solution to the resulting system of congruences using the above algorithm, and the smallest positive solution will be the actual product.

This might not feel faster, but for much larger numbers, it really is. As an aside, here’s one way to make it play nice for parallel processing (which vastly makes things faster). After you’ve computed the congruences of $12$ and $21$ for the different moduli, send the numbers mod 5 to one computer, the numbers mod 7 to another, and the numbers mod 11 to a third (but also send each computer the list of moduli: 5,7,11). Each computer will calculate the product in their modulus and then use the Euclidean algorithm to calculate the inverse of the product of the other two moduli, and multiply these together. Afterwards, the computers resend their data to a central computer, which just adds the result and takes it mod $5 \cdot 7 \cdot 11$ (to get the smallest positive solution). Since mods are fast and all the multiplication is with smaller integers (no bigger than the largest mod, ever), it all goes faster. And since it’s parallelized, you’re replacing a hard task with a bunch of smaller easier tasks that can all be worked on at the same time. Very powerful stuff!

I have actually never seen someone give the optimal running time that would come from this sort of procedure, though I don’t know why. Perhaps I’ll look into that one day.

#### Application 2: Secret Sharing in Networks of People

This is really slick. Let’s lay out the situation: I have a secret. I want you, my students, to have access to the secret, but only if at least six of you decide together that you want access. So I give each of you a message, consisting of a number and a modulus. Using the CRT, I can create a scheme where if any six of you decide you want to open the message, then you can pool your six bits together to get the message. Notice, I mean any six of you, instead of a designated set of six. Further, no five people can recover the message without a sixth in a reasonable amount of time. That’s pretty slick, right?

The basic idea is for me to encode my message as a number $P$ (I use P to mean plain-text). Then I choose a set of moduli, one for each of you, but I choose them in such a way that the product of any $5$ of them is smaller than $P$, but the product of any $6$ of them is greater than $P$ (what this means is that I choose a lot of primes or near-primes right around the same size, all right around the fifth root of $P$). To each of you, I give you the value of $P \mod m_i$ and the modulus $m_i$, where $m_i$ is your modulus. Since $P$ is much bigger than $m_i$, it would take you a very long time to just happen across the correct multiple that reveals a message (if you ever managed). Now, once six of you get together and put your pieces together, the CRT guarantees a solution. Since the product of your six moduli will be larger than $P$, the smallest solution will be $P$. But if only five of you get together, since the product of your moduli is less than $P$, you don’t recover $P$. In this way, we have our secret sharing network.

To get an idea of the security of this protocol, you might imagine if I gave each of you moduli around the size of a quadrillion. Then missing any single person means there are hundreds of trillions of reasonable multiples of your partial plain-text to check before getting to the correct multiple.

A similar idea, but which doesn’t really use the CRT, is to consider the following problem: suppose two millionaires Alice and Bob (two people of cryptological fame) want to see which of them is richer, but without revealing how much wealth they actually have. This might sound impossible, but indeed it is not! There is a way for them to establish which one is richer but with neither knowing how much money the other has. Similar problems exist for larger parties (more than just 2 people), but none is more famous than the original: Yao’s Millionaire Problem.

Alright – I’ll see you all in class.