Lab 9: Bloom Filters

• Due before 23:59 Wednesday, October 26
• Submit using the submit program as 301 lab 09.

1 A new data structure for Sets

There is another common data structure, closely related to Hashtables, which is often used for sets (but not maps!) known as a Bloom Filter. Like Treaps, Bloom Filters are both probablistic and easy to implement. But unlike treaps, Bloom Filters are actually used in practice quite a lot. They are frequently the best solution in practice for storing a set where we don't need to do removals, and where it's OK for the answer to sometimes be wrong (but rarely).

By implementing a Bloom Filter, you'll gain a better understanding of how Hashtables work, you'll learn how to call cryptographic hash functions from within Python, and you'll learn a new data structure which is commonly used (for example, Google keeps a set of web sites which may introduce malware to a system, so that it can warn you before you go to such a page; it does this using a Bloom Filter).

Suppose you wanted to implement a set of strings (such as, like Google, URLs) using a Hashtable. You could do this! You would hash your string, and then insert that key into your array at the resulting index. If there were collisions, you would handle those using either separate chaining or open addressing. Then, to make your contains method work, you would again hash the string, and see if the linked list at that index contains your key (or, if doing open addressing, move along the array until you find your key or see an empty location). That would work great! The problem is, if this is a big set, that is a large array, in which you would store a lot of space-eating strings.

2 How Bloom Filters Work

Bloom Filters solve this problem by using up much less space, but at the expense of not always being correct. In other words, Bloom Filters will tell you either "No, that element is definitely not in the set," or "Yes, that element is probably in the set, but I might be wrong." It does this by NOT storing the strings themselves, but instead by storing whether or not an element has been hashed to a given index. In other words, a Bloom Filter is just an array of bits! Here's an empty Bloom Filter of size 8:

Suppose we then insert a single key, "hello", which hashes to index 2. Well, we would turn that False at index 2 to a True:

Now, if we wanted to know if "hello" was in the set, we would again perform the hash, get index 2, and then see if that element was a True. If it was a False, that element had definitely not been added to the set. If it was True, it either had been added to the set, or some other key had collided at that index, turning it True.

To decrease the chances of that happening, we hash each key several times, using different hash functions, and turn all of those indices to True. For example, suppose we start with an empty Bloom Filter, and use three hash functions:

Here, we've used three hash functions, each of which have given us a different index. So, now suppose we do a contains() on the same key: we'll get the same three indices, and see that all three indices store a True. Therefore, the element is probably in the set. On the other hand, say we do a contains() on a different element. All three hashes would have to collide before we erroneously reported that this element was in the set. Could it happen? Sure. But it won't happen often, as long as the array is long enough.

3 Cryptographic hash functions in Python

So, we're going to need a bunch of hash functions. One (non-cryptographic) hash function for strings is built in, and you merely need to call hash(k). But what about the other two?

Though our hash functions here don't need to be cryptographically secure, it doesn't hurt, either. So what about MD5? Or SHA512? Well, the python module hashlib provides all of these for us, so we don't have to implement them ourselves. Below is an example of how this can be done for the MD5 hash and the string "hello":

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  import hashlib   aKey = "hello"                # This is the string we're going to hash   hashObject = hashlib.md5()    # Builds an MD5 object   byteString = aKey.encode()    # .encode() turns a string into an array of ints   hashObject.update(byteString) # This adds the string to the                               # input to the hash function   dig = hashObject.hexdigest()  # This actually computes the hash, and                               # outputs the result as a hex string   number = int(dig, 16)         # This converts the hex string to an actual integer                               # 16 is the base of the number in the input string   print("The md5 hash of", aKey, "is", number)

For the other two hashes, please use MD5 and SHA512.

4 The assignment

Step 1 (earn up to a B)

Implement a Set of strings with a Bloom Filter. This means you'll make a file called bloom.py with a class called BloomFilter, which has a constructor, which accepts an int representing the size of the array as an argument. This constructor should create a list of Falses, which is that input size. Additionally, it has a method add(self,k) which adds the string k to the set, and a method __contains__(self,k) which returns true or false, representing whether k is in the Set.

Step 2 (earn up to an A)

To save even more space, Bloom Filters are usually not actually implemented with an array of booleans, each of which needlessly take up (at least) an entire byte. Instead, this entire bit array is implemented as a single integer which, when printed out in binary, displays the sequence of Trues and Falses as 1s and 0s.

To do this, you'll need to be able to change and check individual bits in a number. You can do this with bitwise operators. As an example, suppose we have a variable aNum, and we'd like to set the third bit. First we create a number with the third bit set: thirdBit=1<<2 (shift a 1 two bits over, so it's 100), then do a bitwise or: ans = aNum | thirdBit. ans will have its third bit set to 1. Reading a bit requires a similar operation, but with a bitwise and operator &.

Replace your list of booleans with an int, and use bitwise operators to change or check bits when needed. Your program should have the same names as part 1, but I recommend you rename that to something like bloom_array.py so you don't lose your work.

Step 3: Filtering malware sites (Going further)

Use your wonderful Bloom filter to do something useful - check a large number of URL requests against a list of known attack websites.

Specifically, I want you to make a short Python program in a new file called checksites.py that takes two filenames as command-line arguments: the first filename will be a list of "bad" websites (one per line). You should create a Bloom filter of some appropriate size and add each bad website to the filter. THe second filename will be a list of websites to check (one per line). For each of these, you should check whether the website is contained in the Bloom filter or not. If it IS contained in the list of bad websites as reported by your Bloom filter, print out that URL (so it can presumably be blocked).

To aid in this quest, the following tarball contains a list of a few thousand known attack websites as well as a list of the top 1 million websites according to Amazon Alexa. CAUTION: This is real data. Don't visit any of the sites in the bad.txt list, really!!! Here's the tarball: sites.tar.gz.

There are actually 13 websites in the top 1 million that are also known attack sites. If you get this far, document in your README.txt file how large you had to make your Bloom filter so that it blocked at most 100 websites out of the list of 1 million (that is, the 13 actual attack sites plus at most 87 legitimate ones).