Information Structures II CPSC 335 Information Structures II Computer Science University of Calgary Canada

Outline Definition of Hashing Did you know that? Hash functions Collision Resolution Analysis of searching with Hash tables

Introduction to Hashing Approaches to Search Sequential and list methods (lists, tables, arrays). 2. Direct access by key value (hashing) 3. Tree indexing methods.

Introduction to Hashing 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.

Introduction to Hashing

Introduction to Hashing 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.

Introduction of Hashing 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

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 DSS Roger Nebel]

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]

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) We design a perfect hash function to losslessly pack sparse data while retaining efficient random access: Hash table Offset table Hash table Offset table 1282 382 182 1283 353 193 Hash function Applications 2D 3D Simply: (modulo table sizes) Offset table  24372 11632 83333 453 Domain Hash table H 3D textures 3D painting 10243, 46MB, 530fps 20483, 56MB, 200fps Vector images Sprite maps nearest: 7.5MB, 370fps 10242, 500KB, 700fps +900KB, 200fps 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 1.8% Alpha compression Simulation Collision detection 0.9bits/pixel, 800fps 2563, 100fps 10243, 12MB, 140fps

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]

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.

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.

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.

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);

Hash Functions 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 the remaining portion becomes the index. 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 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 function being the value of the remainder. 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 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.

Collision Resolution 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

Collision Resolution - - Hash table size 11 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.

Collision Resolution 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. Probing If the table position given by the hashed key is already occupied, increase the position by some amount, until an empty position is found

Collision Resolution Linear Probing Collision Resolution – Open Addressing Linear Probing new 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.

Collision Resolution the first open space must be used. Collision Resolution – Open Addressing 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

Collision Resolution Quadratic Probing Collision Resolution – Open Addressing Quadratic Probing new 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.

Key-dependent Increments Collision Resolution Collision Resolution – Open Addressing 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.

Key-dependent Increments Collision Resolution Collision Resolution – Open Addressing Key-dependent Increments For 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:

Key-dependent Increments Collision Resolution Collision Resolution – Open Addressing 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.

Collision Resolution Each table position is a linked list 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

Collision Resolution Example: Before chaining: After chaining: Collision Resolution – Chaining Example: Before chaining: After chaining:

Analysis of Searching using Hash Tables 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 Average number of probes for different collision resolution methods: [ The values are for large hash tables, in this case larger than 430]

Analysis of Searching using Hash Tables 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

Some Links to Hashing Animation 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