CPSC 335 Information Structures II Computer Science University of Calgary Canada

CPSC 335

Information Structures IIComputer Science

University of Calgary

Canada

2

Outline

Definition of Hashing Did you know that?

Hash functions

Collision Resolution

Analysis of searching with Hash tables

3

Approaches to Search

1.Sequential and list methods (lists, tables, arrays).

2. Direct access by key value (hashing)

3. Tree indexing methods.

Introduction to Hashing

4

Definition

Hashing is the process of mapping a key value to a position in a table.

A hash function maps key values to positions.

A hash table is an array that holds the records.

Searching in a hash table can be done in O(1) regardless of the hash table size.


5


6

Example of Usefullness

10 stock details, 10 table positions

Stock numbers are between 0 and 1

1000. Using the whole stock

numbers may require 1000 storage

locations and this is an obvious waste of

memory.


7

Applications of Hashing

Compilers use hash tables to keep track of declared variables

A hash table can be used for on-line spelling checkers — if

misspelling detection (rather than correction) is important, an entire

dictionary can be hashed and words checked in constant time

Game playing programs use hash tables to store seen positions,

thereby saving computation time if the position is encountered

again

Hash functions can be used to quickly check for inequality — if

two elements hash to different values they must be different

Storing sparse data

Introduction of Hashing

8

Did you know that? Cryptography was once known only to the key people

in the the National Security Agency and a few academics.

Until 1996, it was illegal to export strong cryptography from the United States.

Fast forward to 2006, and the Payment Card Industry Data Security Standard (PCI DSS) requires merchants to encrypt cardholder information. Visa and MasterCard can levy fines of up to $500,000 for not complying!

Among methods recommended are: Strong one-way hash functions (hashed indexes) Truncation Index tokens and pads (pads must be securely

stored) Strong cryptography [Hashing for fun and profit: Demystifying encryption for PCI

DSSRoger Nebel]

9

Did you know that?

Transport Layer Security protocol on networks (TLS) uses the Rivest, Shamir, and Adleman (RSA) public key algorithm for the TLS key exchange and authentication, and only the Secure Hashing Algorithm 1 (SHA-1) for the key exchange and hashing.

[System cryptography: Use FIPS compliant algorithms for encryption, hashing, and signing, Microsoft TechNews, 2005]

10

Did you know that?

Spatial hashing studies performed at Microsoft Research Redmond combine hashing with computer graphics to create a new set of tools for rendering, mesh reconstruction, and collision optimization (see public poster by Hugues Hoppe on the next slide)

Perfect Spatial Hashing Sylvain Lefebvre Hugues Hoppe(Microsoft Research)

Hash table Offset table Hash table Offset table

Vector images Sprite maps

Alpha compression

3D textures 3D painting

Simulation Collision detection

2D 3D

1282 382 182 1283 353 193

ApplicationsHash function

p

p S

( )s h p

modq p r

[ ]q

Domain Hashtable H

Offsettable

( )h p p p

• Perfect hash on multidimensional data• No collisions ideal for GPU• Single lookup into a small offset table

• Offsets only ~4 bits per defined data

• Access only ~4 instructions on GPU

• Optimized spatial coherence

10243, 46MB, 530fps 20483, 56MB, 200fps

10243, 12MB, 140fps 2563, 100fps

10242, 500KB, 700fps +900KB, 200fps

(modulo table sizes)

0.9bits/pixel, 800fps

1.8%

24372

83333

• We design a perfect hash function to losslessly pack sparse data while retaining efficient random access:

• Simply:

453

nearest: 7.5MB, 370fps

11632

12

Did you know that?

Combining hashing and encryption provides a much stronger tool for database and password protection.

http://msdn.microsoft.com/msdnmag/issues/03/08/SecurityBriefs/

[Security Briefs, SMDN Magazine]

13

How can I store passwords in a custom user database?

There are several options. The simplest might leave you with cleartext passwords. The following example is XML:

<users> <user name='Alice' password='7&y2si(V1dX'/> <user name='Bob' password='mary'/> <user name='Fred' password='mary'/> </users>

After implementing something like this, you'll likely feel rather uncomfortable that all those passwords are sitting there in one file, in the clear. If you don't feel uncomfortable, you should!

The first approach you might take to protect these passwords is to encrypt them. That's better than nothing, but it's not the best solution. In order to validate a user's password, you need the encryption key, which means it needs to be available on the machine where the passwords are processed.

14

How can I store passwords in a custom user database?

A better solution that doesn't require any key at all is a one-way function! A cryptographic hash algorithm like SHA-1 or MD5 is a sophisticated one-

way function that takes some input and produces a hash value as output, but more resistant to collisions.

It's incredibly unlikely that you'd find two messages that hash to the same value! As a one-way function, it can't be reversed. There is no key that you need to store. You hash the password before storing it in the database:

<users> <user name='Alice' password='D16E9B18FA038...'/> <user name='Bob' password='5665331B9B819...'/> <user name='Fred' password='5665331B9B819...'/> </users>

Now when you receive the cleartext password and need to verify it, you don't decrypt the stored password for comparison. Instead, you hash the password provided by the user and compare the result with your stored hash.

If an attacker manages to steal your password database, he won't be able to use the passwords, as they can't be reversed back into cleartext.

15

Salt

But look closely at Bob and Fred's hashed passwords. If the attacker happened to be Fred, he now knows that Bob uses the same password he does. What luck! Even without this sort of luck, a bad guy can perform a dictionary attack against the hashed passwords to find matches.

The usual way a dictionary attack is performed is to get a list of commonly used passwords, like the lists you'll find at ftp://coast.cs.purdue.edu/pub/dict/wordlists, and calculate the hash for each. Now the attacker can compare the hash values of his dictionary with those in the password database. Once he finds a match, he looks up the corresponding password.

To slow down the attack, use salt. Salt is a way to season the passwords before hashing them, making the attacker's precomputed dictionary useless. Here's how it's done. Whenever you add an entry to the database, you calculate a random string of digits to be used as salt. When you want to calculate the hash of Alice's password, you look up the salt value for Alice's account, prepend it to the password, and hash them together. The resulting database looks like this:

<users> <user name='Alice' salt='Tu72*&' password='6DB80AE7...'/> <user name='Bob' salt='N5sb#X' password='096B1085...'/> <user name='Fred' salt='q-V3bi' password='9118812E...'/> </users>

Note that now there is no way to tell that Bob and Fred are using the same password.

16

Salt: example of usage

Below is a C# example of using hash library [Keith Brown, Hashing Passwords, The AllowPartiallyTrustedCallers Attribute]:

string password = Console.ReadLine(); SaltedHash sh = SaltedHash.Create(password); // imagine storing the salt and hash in a database string salt = sh.Salt; string hash = sh.Hash; Console.WriteLine("Salt: {0}", salt); Console.WriteLine("Hash: {0}", hash); // after looking up salt and hash, verify a password SaltedHash ver = SaltedHash.Create(salt, hash); bool isValid = ver.Verify(password);

17

Hash Functions

Hashing is the process of chopping up the key and

mixing it up in various ways in order to obtain an index

which will be uniformly distributed over the range of

indices -- hence the ‘hashing’.

There are several common ways of doing this:

Truncation Folding Modular Arithmetic

Hash Functions

18

Hash Functions – Truncation

Truncation is a method in which parts of the key are ignored and

the remaining portion becomes the index. - For this, we take the given key and produce a hash

location by taking portions of the key (truncating the key).

Example – If a hash table can hold 1000 entries and an 8-digit

number is used as key, the 3rd, 5th and 7th digits starting

from the left of the key could be used to produce the index.

- e.g. .. Key is 62538194 and the hash location is 589. - Advantage: Simple and easy to implement.

Problems: Clustering and repetition.

Hash Functions

19

Hash Functions – Folding

Folding breaks the key into several parts and combines the parts to form an index.

- The parts may be recombined by addition, subtraction, multiplications and may have to be truncated as well.

- Such a process is usually better than truncation by itself since it produces a better distribution: all of the digits in the key are considered.

- Using a key 62538194 and breaking it into 3 numbers using the first 3 and the last 2 digits produced 625, 381 and 94. These could be added to get 1100 which could be truncated to 100.

They could be also be multiplied together and then three digits chosen

from the middle of the number produced.

Hash Functions

20

Hash Functions – (Modular Arithmetic)

Modular Arithmetic process essentially assures that the index produced is within a specified range. For this, the key is converted to an integer which is divided by the range of the index with the resulting

function being the value of the remainder.

Uses: biometrics, encryption, compression - If the value of the modulus is a prime number, the

distribution of indices obtained is quite uniform. - A table whose size is some number which has many

factors provides the possibility of many indices which are the same, so the size should be a prime number.

Hash Functions

21

Good Hash Functions

Hash functions which use all of the key are almost always better than those which use only some of the key.

- When only portions are used, information is lost and therefore the

number of possibilities for the final key are reduced.

- If we deal with the integer its binary form, then

the number of pieces that can be manipulated by the hash

function is greatly increased.

Hash Functions

22

Collision

It is obvious that no matter what function is used, the possibility

exists that the use of the function will produce an index which is a

duplicate of an index which already exists. This is a Collision.

Collision resolution strategy:

- Open addressing: store the key/entry in a different position

- Chaining: chain together several keys/entries in each position


23

Collision - Example

- - Hash table size 11 - - Hash function: key mod hash size

So, the new positions in the hash table are:

Some collisions occur with this hash function.


24

Collision Resolution – Open Addressing

Resolving collisions by open addressing is resolving the problem by

taking the next open space as determined by rehashing the key

according to some algorithm.

Two main open addressing collision resolution techniques:

- - Linear probing: increase by 1 each time [mod table size!]

- - Quadratic probing: to the original position, add 1, 4, 9, 16,…

also in some cases key-dependent increment technique is used.


ProbingIf the table position given by the hashed key is already occupied, increase the position by some amount, until an empty position is found

25


Linear Probingnew position = (current position + 1) MOD

hash size

Example – Before linear probing:

After linear probing:

Problem – Clustering occurs, that is, the used spaces tend to appear in groups

which tends to grow and thus increase the search time to reach an open space.


26


In order to try to avoid clustering, a method which does not look for

the first open space must be used.

Two common methods are used –

- - Quadratic Probing - - Key-dependent Increments


27


Quadratic Probingnew position = (collision position + j2) MOD

hash size { j = 1, 2, 3,

4, ……}Example – Before quadratic probing:

After quadratic probing:

Problem – Overflow may occurs when there is still space in the hash table.


28


Key-dependent Increments

This technique is used to solve the overflow problem of the quadratic probing method.

These increments vary according to the key used for the hash

function. If the original hash function results in a good

distribution, then key- dependent functions work quite well for rehashing and

all locations in the table will eventually be probed for a free position.

Key dependent increments are determined by using the key to

calculate a new value and then using this as an increment to determine

successive probes.


29


Key-dependent IncrementsFor example, since the original hash function was key Mod 11, we

might choose a function of key DIV 11 to find the increment. Thus the hash function becomes - -

new position = current position + ( key DIV 11) MOD 11

Example – Before key-dependent increments:

After key-dependent increments:


30


Key-dependent Increments In all of the closed hash functions it is important to

ensure that an increment of 0 does not arise.

- - If the increment is equal to hash size the same position will be probed all the time, so this value cannot be used.

If we ensure that the hash size is prime and the divisors for the open and

closed hash are prime, the rehash function does not produce a 0

increment, then this method will usually access all positions as does the linear

probe.

- - Using a key-dependent method usually result reduces clustering and

therefore searches for an empty position should not be as long as for the

linear method.


31

Collision Resolution – Chaining

Each table position is a linked list

Add the keys and entries anywhere in the

list (front easiest)Advantages over open addressing:

- Simpler insertion and removal

- Array size is not a limitation (but should still minimize collisions: make table size roughly equal to expected number of keys and entries)

Disadvantage - Memory overhead is large if

entries are small


32

Collision Resolution – Chaining

Example:

Before chaining:

After chaining:


33

In analyzing search efficiency, the average is usually used. Searching with

hash tables is highly dependent on how full the table is since as the table

approaches a full state, more rehashes are necessary. The proportion of the

table which is full is called the Load Factor.

- - When collisions are resolved using open addressing, the maximum load

factor is 1. - - Using chaining, however, the load factor can be greater

than 1 when the table is full and the linked list attached to each hash

address has more than one element.

- Chaining consistently requires fewer probes than open addressing.

- Traversal of the linked list is slow and if the records are small, it may be just

as well to use open addressing. - Chaining is the best under two conditions --- when the

number of unsuccessful searches is large or when the records are

large. - Open addressing would likely be a reasonable choice

when most searches are likely to be successful, the load factor is moderate and

the records are relatively small.

Analysis of Searching using Hash Tables

34

Average number of probes for different collision resolution methods:

[ The values are for large hash tables, in this case larger than 430]


35

When are other representations more suitable than hashing:

Hash tables are very good if there is a need for many searches in a

reasonably stable table

Hash tables are not so good if there are many insertions and deletions, or if table traversals are needed — in this case, AVL trees are better

If there are more data than available memory then use a B-tree

Also, hashing is very slow for any operations which require the entries to be sorted e.g. Find the minimum key


36

Links for interactive hashing example:

http://www.engin.umd.umich.edu/CIS/course.des/cis350/hashing/WEB/HashApplet.htm

http://www.cs.auckland.ac.nz/software/AlgAnim/hash_tables.html

http://www.cse.yorku.ca/~aaw/Hang/hash/Hash.html

http://www.cs.pitt.edu/~kirk/cs1501/animations/Hashing.html

Some Links to Hashing Animation

Documents

CPSC 335 Information Structures II Computer Science University of Calgary Canada