alice | Java AES encryption library | Encryption library
kandi X-RAY | alice Summary
kandi X-RAY | alice Summary
Alice is a Java AES/DES encryption library for working with byte arrays, files, and streams. Various key lengths, block modes, padding schemes, key deriviation functions, and Message Authentication Codes (MAC) are available. See the javadoc for more information. Alice provides an easy wrapper around the javax.crypto cipher suite for symmetric key encryption. if a MAC algorithm is selected, additional Authenticated Encryption is performed using an encrypt-then-mac scheme. Key Lengths (in bits). Password Based Key Derivation Functions (PBKDF). MAC Algorithm (Authenticated Encryption). GCM Tag Length (in bits). Iterations (used when the PBKDF = PBKDF2WithHmacSHA{Length}).
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Top functions reviewed by kandi - BETA
- Decrypts a file using the given password
- Derive sha key from password
- Converts a character array to a byte array
- Derive AES key
- Decrypt an input stream using the specified password
- Derive sha key from password
- Converts a character array to a byte array
- Derive AES key
- Decrypt a byte array using the given password
- Derive sha key from password
- Converts a character array to a byte array
- Derive AES key
- Encrypts the given file using the given password
- Generate initialization vector
- Encrypt the input stream using the provided password
- Generate initialization vector
- Encrypts a byte array using the given password
- Generate initialization vector
- Generates an AES key
alice Key Features
alice Examples and Code Snippets
Lines of Code : 6License : Permissive (Apache-2.0)
CopyAliceContext aliceContext = new AliceContextBuilder()
.setAlgorithm(AliceContext.Algorithm.AES)
.setMode(AliceContext.Mode.GCM)
.setIvLength(ivLength) // e.g. 12
.setGcmTagLength(AliceContext.GcmTagLength.BITS_128)
// Initialize an Alice instance with defaults:
// Cipher = AES/256/CTR/NoPadding
// Key Derivation = PBKDF2WithHmacSHA512/Iterations(10000)
// MAC = HmacSHA512
Alice alice = new Alice(new AliceContextBuilder().build());
AliceContext aliceContext = new AliceContextBuilder()
.setAlgorithm(AliceContext.Algorithm.AES)
.setMode(AliceContext.Mode.CBC) // or AliceContext.Mode.CTR
.setIvLength(16)
.build()
Community Discussions
Trending Discussions on alice
QUESTION
I have two tables: Contacts and Messages. I'd like to fetch a Chat structure that doesn't belong to any table (in other words: there's no Chats table I just want to build a query) This query should contain:
- A
contactI'm referring to in that chat - A
lastMessagebetween me and that contact (If I don't have a last message w/ that contact - I should get no result from that contact specifically) unreadCountthat tells how many messages inside that conversation are not read yet.
- Contacts
uniqueId(Blob)username(Text)
- Messages
isRead(Bool)sender(Blob)receiver(Blob)timestamp(Integer)
The farthest I got was this:
...ANSWER
Answered 2022-Feb-16 at 18:08I think this has all you want in it ... EDIT: First pass missed Unread count!
QUESTION
Project Alice generates Java source code, stores it in sources.jar, then uploads it to a Maven repository. Project Bob pulls sources.jar down and needs to use it when compiling. Bob does not know that Alice exists, only where to find sources.jar.
Versions: JDK 11, Gradle 7.3.1, IntelliJ IDEA 2021.3.1
ProblemMaking gradle (and IntelliJ's IDEA) build using source files embedded in a JAR file. To be clear, the JAR file contents resemble:
...ANSWER
Answered 2022-Feb-07 at 22:28I first believed it wasn’t possible to use a JAR file containing uncompiled Java code as additional sources in IntelliJ. After a few tries I could eventually configure it in the UI, though, thanks to the pointer from your “Content Root” section. A bit of fiddling with the IDEA plugin later, I could finally come up with a fully working solution:
QUESTION
I'm fairly new to R and am sure there's a way to do the following without using loops, which I'm more familiar with.
Take the following example where you have a bunch of names and fruits each person likes:
...ANSWER
Answered 2022-Feb-06 at 17:53df %>%
unnest(everything()) %>%
xtabs(~., .) %>%
as.data.frame.matrix() %>%
rownames_to_column('name')
name apple banana pear
1 Alice 1 0 1
2 Bob 1 1 0
QUESTION
I would like to automatically generate some sort of log of all the database changes that are made via the Django shell in the production environment.
We use schema and data migration scripts to alter the production database and they are version controlled. Therefore if we introduce a bug, it's easy to track it back. But if a developer in the team changes the database via the Django shell which then introduces an issue, at the moment we can only hope that they remember what they did or/and we can find their commands in the Python shell history.
Example. Let's imagine that the following code was executed by a developer in the team via the Python shell:
...ANSWER
Answered 2022-Jan-19 at 09:20You could use django's receiver annotation.
For example, if you want to detect any call of the save method, you could do:
QUESTION
In Python I have a group of lists that track information about some users:
...ANSWER
Answered 2022-Jan-09 at 22:13# Dict for holding your data
data = dict()
# Put all your stuff into data
for id, name, email in zip( user_id, user_name , user_email):
data[ id ] = { "id": id , "username" : name , "email" : email }
# Function for lookup up by key and value
def lookup_info( key_name , lookup_value , data ):
'''
Takes a key name, a lookup value and a dictionary of data.
Returns the dictionary item
'''
for k,v in data.items():
if v[ key_name ] == lookup_value:
return( data[ k ] )
QUESTION
I would like to order ElasticSearch query results based on the percentage of matches for a nested field.
For example, let's suppose I have an ElasticSearch index strucutured as follows:
...ANSWER
Answered 2022-Jan-01 at 15:54script_score seems to do the job:
QUESTION
Here is my input DataFrame
...ANSWER
Answered 2021-Dec-23 at 13:13Your first groupby was correct, after that you want to sort your values based on State and Count.
Then you group again solely on the state and fetch the head(2). If you want, you can (re)set your index to State and City.
QUESTION
I am working on some sentence formation like this:
...ANSWER
Answered 2021-Dec-12 at 17:53You can first replace the dictionary keys in sentence to {} so that you can easily format a string in loop. Then you can use itertools.product to create the Cartesian product of dictionary.values(), so you can simply loop over it to create your desired sentences.
QUESTION
I couldn't find a question similar to the one that I have here. I have a very large named list of named vectors that match column names in a dataframe. I would like to use the list of named vectors to replace values in the dataframe columns that match each list element's name. That is, the name of the vector in the list matches the name of the dataframe column and the key-value pair in each vector element will be used to recode the column.
Reprex below:
...ANSWER
Answered 2021-Dec-13 at 04:44One work around would be to use your map2_dfr code, but then bind the columns that are needed to the map2_dfr output. Though you still have to drop the names column.
QUESTION
I'm working on a pipeline of manipulations on a pandas dataframe in class, and I'm wondering what the good steps are for concatenating some procedures one after the other - should I copy and recreate the original dataframe, or just change it in place?
According to the pandas documentation, working with views is not always recommended, and I'm not sure if this is the case here.
For example:
...ANSWER
Answered 2021-Dec-13 at 11:58Actually, both of your implementations (the three of them) are mutating your class's df in-place and thus are practically equivalent in matters of their effect on the class. The difference is that in the first and second implementations, after mutating in-place you are returning the mutated df. In the first implementation assigning it to different class attributes (aliases as you call them) and in the second implementation assigning it to itself (which has no effect).
So, if any, the third implementation is the more valid one.
Nonetheless, in case you want to achieve a more functional syntax, which one can say is the pandas way "for concatenating some procedures one after the other" as you stated, you can use functions instead of methods and pass the self.df to them as a parameter. Then you can assign the results to new columns in your self.df.
For example:
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