[1] Since the hashing requires not only a password but also a salt (or a user name), which is unique for each user, the attack speed for such hashes will decline proportionally to their count (for example, attacking 100 hashes will go 100 times slower than attacking one hash).
Different Types of Hash Codes-How to Find Which Hash types
If you are transferring a file from one computer to another, how do you ensure that the copied file is the same as the source? One method you could use is called hashing, which is essentially a process that translates information about the file into a code. Two hash values (of the original file and its copy) can be compared to ensure the files are equal.
Hashing is an algorithm that calculates a fixed-size bit string value from a file. A file basically contains blocks of data. Hashing transforms this data into a far shorter fixed-length value or key which represents the original string. The hash value can be considered the distilled summary of everything within that file.
A good hashing algorithm would exhibit a property called the avalanche effect, where the resulting hash output would change significantly or entirely even when a single bit or byte of data within a file is changed. A hash function that does not do this is considered to have poor randomization, which would be easy to break by hackers.
A good hash algorithm should be complex enough such that it does not produce the same hash value from two different inputs. If it does, this is known as a hash collision. A hash algorithm can only be considered good and acceptable if it can offer a very low chance of collision.
One main use of hashing is to compare two files for equality. Without opening two document files to compare them word-for-word, the calculated hash values of these files will allow the owner to know immediately if they are different.
In some situations, an encrypted file may be designed to never change the file size nor the last modification date and time (for example, virtual drive container files). In such cases, it would be impossible to tell at a glance if two similar files are different or not, but the hash values would easily tell these files apart if they are different.
In the backup and synchronization software, SyncBackPro/SE/Free, hashing is mainly used for file integrity checks during or after a data transfer session. For example, a SyncBack user can turn on file verification (Modify profile > Copy/Delete) or use a slower but more reliable method (Modify profile > Compare Options) which will enable hashing to check for file differences. Different hash functions will be used depending on which option is used and where the backup files are located.
2BrightSparks also has a utility program called HashOnClick that can be used to ensure files are identical. HashOnClick is part of OnClick Utilities, which is completely free. Several types of hashing algorithms are available in HashOnClick.
In conclusion, hashing is a useful tool to verify files are copied correctly between two resources. It can also be used to check if files are identical without opening and comparing them. To find out more about hashing, please visit the Wikipedia page.
Salting hashes sounds like one of the steps of a hash browns recipe, but in cryptography, the expression refers to adding random data to the input of a hash function to guarantee a unique output, the hash, even when the inputs are the same. Consequently, the unique hash produced by adding the salt can protect us against different attack vectors, such as hash table attacks, while slowing down dictionary and brute-force offline attacks.
Attacker gets DB. Sees duplicate hashes. Attacker can arrive to conclusion that there's no salts or using a weak algo to hash the passwords. If they find a lot of the same hashes, sign that server has a default password and every new acct has a default password. The kinds of attacks we're talking about here are offline attacks against compromised/exfiltrated data.
An attacker has two types of tools at disposal: hash table and rainbow table. Definition of both and how they can help with cracking table. Hash tables to be exhausted first. Additional results use a rainbow.
Since time and space are limited, the attacker that designs and computes the hash table may want to process the most commonly used passwords first. Here is where alice and bob could be at a much higher risk if dontpwnme4 is in that common-password list. Large common-password databases are created using frequency analysis across passwords collected from different publicly leaked breaches.
To mitigate the damage that a hash table or a dictionary attack could do, we salt the passwords. According to OWASP Guidelines, a salt is a value generated by a cryptographically secure function that is added to the input of hash functions to create unique hashes for every input, regardless of the input not being unique. A salt makes a hash function look non-deterministic, which is good as we don't want to reveal duplicate passwords through our hashing.
Different users, same password. Different salts, different hashes. If someone looked at the full list of password hashes, no one would be able to tell that Alice and Bob both use the same password. Each unique salt extends the password farm1990M0O and transforms it into a unique password. Additionally, when a user changes their password, the service should also generate a new salt.
As storage permits, use a 32-byte or 64-byte salt with the actual size dependent on the protection function. A longer salt effectively increases the computational complexity of attacking passwords which in turn increases the candidate set exponentially. A longer salt also increases the space required to store hash tables while decreasing the possibility that such a table exists in the wild.
Now if am correct, I have the following question:The HashMap internally uses the hashcode of the object. So if two objects can have the same hashcode, then how can the HashMap track which key it uses?
It has a number of "buckets" which it uses to store key-value pairs in. Each bucket has a unique number - that's what identifies the bucket. When you put a key-value pair into the map, the hashmap will look at the hash code of the key, and store the pair in the bucket of which the identifier is the hash code of the key. For example: The hash code of the key is 235 -> the pair is stored in bucket number 235. (Note that one bucket can store more then one key-value pair).
Now you can see how this is very efficient for looking up key-value pairs in a map: by the hash code of the key the hashmap immediately knows in which bucket to look, so that it only has to test against what's in that bucket.
If two keys are the same (equals() returns true when you compare them), their hashCode() method must return the same number. If keys violate this, then keys that are equal might be stored in different buckets, and the hashmap would not be able to find key-value pairs (because it's going to look in the same bucket).
If two keys are different, then it doesn't matter if their hash codes are the same or not. They will be stored in the same bucket if their hash codes are the same, and in this case, the hashmap will use equals() to tell them apart.
It's perfectly legal for two unequal objects to have the same hash code. It's used by HashMap as a "first pass filter" so that the map can quickly find possible entries with the specified key. The keys with the same hash code are then tested for equality with the specified key.
You wouldn't want a requirement that two unequal objects couldn't have the same hash code, as otherwise that would limit you to 232 possible objects. (It would also mean that different types couldn't even use an object's fields to generate hash codes, as other classes could generate the same hash.)
Sometimes it might happen that hash codes for 2 different objects are the same. In this case, two objects will be saved in one bucket and will be presented as a linked list. The entry point is the more recently added object. This object refers to another object with the next field and so on. The last entry refers to null.
get(key): HashMap uses Key Object's hashcode to find out bucket location and then call keys.equals() method to identify correct node in LinkedList and return associated value object for that key in Java HashMap.
The hashcode determines which bucket for the hashmap to check. If there is more than one object in the bucket then a linear search is done to find which item in the bucket equals the desired item (using the equals()) method.
So here we see that if both the objects S1 and S2 have different content, then we are pretty sure that our overridden Hashcode method will generate different Hashcode(116232,11601) for both objects. NOW since there are different hash codes, so it won't even bother to call EQUALS method. Because a different Hashcode GUARANTEES DIFFERENT content in an object.
and this will be stored as linked list for every object which have the same hashcode, but a TRIEFY_THRESHOLD is specified with value 6. so after this reaches, linked list is converted to the balanced tree(red-black tree) with first element as the root.
Sometimes it might happen that hashCodes for 2 different objects are the same. In this case 2 objects will be saved in one bucket and will be presented as LinkedList. The entry point is more recently added object. This object refers to other object with next field and so one. Last entry refers to null.When you create HashMap with default constructor
HashMap get(Key k) method calls hashCode method on the key object and applies returned hashValue to its own static hash function to find a bucket location(backing array) where keys and values are stored in form of a nested class called Entry (Map.Entry) . So you have concluded that from the previous line that Both key and value is stored in the bucket as a form of Entry object . So thinking that Only value is stored in the bucket is not correct and will not give a good impression on the interviewer . 2ff7e9595c
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