Patent US6122379A successfully refreshed from Google Patents.
US6122379A
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Method and apparatus for performing simultaneous data compression and encryption
Assignee: GANDALF TECHNOLOGIES Inc
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Filing: 1997-05-30
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Granted: 2000-09-19
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Expires: 2017-05-30
Abstract
Abstract A method and apparatus for performing simultaneous data compression and encryption both compresses and encrypts data in a single step. The invention includes a novel modeling method for use in conjunction with a coder, such as an arithmetic coder. The modeling method introduces randomness into the compression process, with the result that the output stream is secure.
Inventors
- 👤 Abbulkader Omar Barbir
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 14:31
CPC Classifications
H; H03; H03M; H03M7/00; H03M7/30; H03M7/3084; H03M7/40; H03M7/42; H04; H04L; H04L9/00; H04L9/06; H04L9/065; H04L9/0656; H04L9/0662; H04L2209/00; H04L2209/08; H04L2209/30; H04L2209/34
Claims Analysis
Total Claims: 34
Claim 1
Independent Claim
62 words
A method for utilizing a processor to change the form of input data having symbols, comprising the steps of: a) providing the input data to the processor; b) processing the input data in the processor by compressing and encrypting the input data in one step to generate compressed and encrypted data; and c) applying the compressed and encrypted data to a medium.
Claim 2
Independent Claim
62 words
A method for utilizing a processor to change the form of input data having symbols, comprising the steps of: a) providing the input data to the processor; b) processing the input data in the processor by compressing and encrypting the input data in one step to generate compressed and encrypted data; and c) applying the compressed and encrypted data to a medium.
Claim 3
Dependent Claim
40 words
A method as defined in claim 1, wherein step (b) comprises the step of: b) processing the input data in the processor by compressing and introducing randomness into the input data in one step to generate compressed and encrypted data.
Claim 4
Dependent Claim
77 words
A method as defined in claim 2, comprising the steps of: a) providing the input data to the processor; b) processing the input data in the processor by implementing a learning modeling method having at least one state that is randomly updated; c) processing the input data in the processor by implementing a back end coder to generate compressed and encrypted data by coding the symbols; and d) applying the compressed and encrypted data to a medium.
Claim 5
Dependent Claim
44 words
A method as defined in claim 3, wherein step (b) comprises the step of: b) processing the input data in the processor by implementing a learning modeling method having at least one state that is updated after a random number of symbols are learned.
Claim 6
Dependent Claim
25 words
A method as defined in claim 4, wherein the modeling method is a learning semi-adaptive modeling method having a static stage and an adaptive stage.
Claim 7
Dependent Claim
27 words
A method as defined in claim 5, wherein the at least one state is updated after a random number of symbols are processed in the static stage.
Claim 8
Dependent Claim
80 words
A method as defined in claim 4, wherein steps (b) and (c) comprise the steps of: b) processing the input data in the processor by implementing a learning modeling method comprising partitioning the input data into blocks having a random number of symbols, and generating probabilistic estimates from at least one block; c) processing the input data in the processor by implementing a back end coder to generate compressed and encrypted data by coding the symbols using the probabilistic estimates.
Claim 9
Dependent Claim
61 words
A method as defined in claim 7, wherein step (b) comprises the step of: b) processing the input data in the processor by implementing a learning modeling method comprising partitioning the input data into blocks having a random number of symbols, each symbol having a frequency, and generating probabilistic estimates of the frequencies of the symbols from at least one block.
Claim 10
Dependent Claim
78 words
A method as defined in claim 8, wherein step (b) comprises the step of: b) processing the input data in the processor by implementing a learning modeling method comprising partitioning the input data into blocks having a random number of symbols, each symbol having a frequency, biasing the frequency of at least one symbol in at least one block by a random number, and generating probabilistic estimates of the frequencies of the symbols from at least one block.
Claim 11
Dependent Claim
64 words
A method as defined in claim 7, wherein step (b) comprises the step of: b) processing the input data in the processor by implementing a learning modeling method comprising partitioning the input data into at least one segment having a random number of symbols, partitioning each segment into blocks having a random number of symbols, and generating probabilistic estimates from at least one block.
Claim 12
Dependent Claim
19 words
A method as defined in claim 7, wherein the learning modeling method comprises generating probabilistic estimates from each block.
Claim 13
Dependent Claim
32 words
A method as defined in claim 7, wherein the number of symbols learned before the at least one state is updated is determined by a random number generator initialized with a seed.
Claim 14
Dependent Claim
22 words
A method as defined in claim 12, wherein the random number generator is a stream cipher and the seed is a key.
Claim 15
Dependent Claim
32 words
A method as defined in claim 1, further comprising between steps (a) and (b) the step of: processing the input data in the processor by implementing at least one front end coder.
Claim 16
Dependent Claim
23 words
A method as defined in claim 7, the modeling method further comprising generating at least one random symbol in at least one block.
Claim 17
Dependent Claim
16 words
A method as defined in claim 3, wherein the back end coder is an arithmetic coder.
Claim 18
Independent Claim
67 words
An apparatus for utilizing a processor to change the form of input data having symbols, comprising: a) means for obtaining the input data at the processor; b) means at the processor for processing the input data, comprising means for compressing and encrypting the input data in one step to generate compressed and encrypted data; and c) means for applying the compressed and encrypted data to a medium.
Claim 19
Independent Claim
67 words
An apparatus for utilizing a processor to change the form of input data having symbols, comprising: a) means for obtaining the input data at the processor; b) means at the processor for processing the input data, comprising means for compressing and encrypting the input data in one step to generate compressed and encrypted data; and c) means for applying the compressed and encrypted data to a medium.
Claim 20
Dependent Claim
35 words
An apparatus as defined in claim 17, wherein the means for processing the input data comprises means for compressing and introducing randomness into the input data in one step to generate compressed and encrypted data.
Claim 21
Dependent Claim
88 words
An apparatus as defined in claim 18, comprising: a) means for obtaining the input data at the processor; b) modeler means at the processor for processing the input data, comprising means for implementing a learning modeling method having at least one state that is randomly updated; c) coder means at the processor for processing the input data, comprising means for implementing a back end coder to generate compressed and encrypted data by coding the symbols; and d) means for applying the compressed and encrypted data to a medium.
Claim 22
Dependent Claim
35 words
An apparatus as defined in claim 19, wherein the modeler means comprises means for implementing a learning modeling method having at least one state that is updated after a random number of symbols are learned.
Claim 23
Dependent Claim
25 words
An apparatus as defined in claim 20, wherein the modeling method is a learning semi-adaptive modeling method having a static stage and an adaptive stage.
Claim 24
Dependent Claim
27 words
An apparatus as defined in claim 21, wherein the at least one state is updated after a random number of symbols are processed in the static stage.
Claim 25
Dependent Claim
72 words
An apparatus as defined in claim 20, wherein the modeler means comprises means for implementing a learning modeling method comprising means for partitioning the input data into blocks having a random number of symbols, and means for generating probabilistic estimates from at least one block; and wherein the coder means comprises means for implementing a back end coder to generate compressed and encrypted data by coding the symbols using the probabilistic estimates.
Claim 26
Dependent Claim
56 words
An apparatus as defined in claim 23, wherein the modeler means comprises means for implementing a learning modeling method comprising means for partitioning the input data into blocks having a random number of symbols, each symbol having a frequency, and means for generating probabilistic estimates of the frequencies of the symbols from at least one block.
Claim 27
Dependent Claim
75 words
An apparatus as defined in claim 24, wherein the modeler means comprises means for implementing a learning modeling method comprising means for partitioning the input data into blocks having a random number of symbols, each symbol having a frequency, means for biasing the frequency of at least one symbol in at least one block by a random number, and means for generating probabilistic estimates of the frequencies of the symbols from at least one block.
Claim 28
Dependent Claim
61 words
An apparatus as defined in claim 23, wherein the modeler means comprises means for implementing a learning modeling method comprising means for partitioning the input data into at least one segment having a random number of symbols, means for partitioning each segment into blocks having a random number of symbols, and means for generating probabilistic estimates from at least one block.
Claim 29
Dependent Claim
25 words
An apparatus as defined in claim 23, wherein the means for implementing the learning modeling method comprises means for generating probabilistic estimates from each block.
Claim 30
Dependent Claim
32 words
An apparatus as defined in claim 23, wherein the number of symbols learned before the at least one state is updated is determined by a random number generator initialized with a seed.
Claim 31
Dependent Claim
22 words
An apparatus as defined in claim 28, wherein the random number generator is a stream cipher and the seed is a key.
Claim 32
Dependent Claim
25 words
An apparatus as defined in claim 17, further comprising means at the processor for processing the input data comprising at least one front end coder.
Claim 33
Dependent Claim
25 words
An apparatus as defined in claim 23, the modeler means further comprising means for generating at least one random symbol in at least one block.
Claim 34
Dependent Claim
16 words
An apparatus as defined in claim 19, wherein the back end coder is an arithmetic coder.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2003-09-19 | 2004-03-20 | 2004-09-19 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2007-09-19 | 2008-03-20 | 2008-09-19 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2011-09-19 | 2012-03-20 | 2012-09-19 | $7700.00 | Window Closed / Lapsed |