Bloom filters are considered an application of hash tables. Bloom filters are us
ID: 3692423 • Letter: B
Question
Bloom filters are considered an application of hash tables. Bloom filters are used for proactive password cracking - to identify whether a password entered by a user at the time of registration is vulnerable for cracking or not. To do so, the Bloom filter maintains the hash values for a potentially vulnerable list of passwords (commonly used passwords, words from dictionary, etc) and if the hash value of the user entered password matches to those in the Bloom filter, the user password is not accepted as part of the registration process and the user is forced to choose a password whose hash value does not match to the one in the Bloom filter. For the purpose of this project, you could envision a Bloom filter that calculates the hash value for a word as the sum of the values of the characters in the word divided by a prime number; the size of the Bloom filter (hash table) is the magnitude of the divisor prime number. You can assume either a closed or open hash table. A false positive scenario is one wherein the user entered password is not vulnerable; but a Bloom filter flags the password as vulnerable. A false negative scenario is one wherein the user entered password is vulnerable; but a Bloom filter does not flag the password as vulnerable. Given the above description of Bloom filters, prove that the Bloom filters cannot have a false negative scenario, but could have a false positive scenario. You could show your proof with examples and record a video. Prove that when Breadth First Search (BFS) is conducted on a graph starling from a particular vertex s, we are guaranteed to find the minimum hop path (paths with the minimum number of intermediate edges) from s to every other vertex in the graph.Explanation / Answer
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