In computer science, a hash collision or hash clash is when two pieces of data in a hash table share the same hash value. The hash value in this case is derived from a hash function which takes a data input and returns a fixed length of bits. Although hash algorithms have been created with the intent of being collision resistant, they can still sometimes map different data to the same hash (by virtue of the pigeonhole principle). Malicious users can take advantage of this to mimic, access, or alter data. Due to the possible negative applications of hash collisions in data management and computer security (in particular, cryptographic hash functions), collision avoidance has become an important topic in computer security. Background Hash collisions can be unavoidable depending on the number of objects in a set and whether or not the bit string they are mapped to is long enough in length. When there is a set of n objects, if n is greater than |R|, which in this case R is the range of the hash value, the probability that there will be a hash collision is 1, meaning it is guaranteed to occur. Another reason hash collisions are likely at some point in time stems from the idea of the birthday paradox in mathematics. This problem looks at the probability of a set of two randomly chosen people having the same birthday out of n number of people. This idea has led to what has been called the birthday attack. The premise of this attack is that it is difficult to find a birthday that specifically matches your birthday or a specific birthday, but the probability of finding a set of any two people with matching birthdays increases the probability greatly. Bad actors can use this approach to make it simpler for them to find hash values that collide with any other hash value – rather than searching for a specific value. The impact of collisions depends on the application. When hash functions and fingerprints are used to identify similar data, such as homologous DNA sequences or similar audio files, the functions are designed so as to maximize the probability of collision between distinct but similar data, using techniques like locality-sensitive hashing. Checksums, on the other hand, are designed to minimize the probability of collisions between similar inputs, without regard for collisions between very different inputs. Instances where