Classifications

• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
  • G06F21/10—Protecting distributed programs or content, e.g. vending or licensing of copyrighted material ; Digital rights management [DRM]
  • G06F21/16—Program or content traceability, e.g. by watermarking
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
  • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
  • H04L9/321—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving a third party or a trusted authority
  • H04L9/3213—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving a third party or a trusted authority using tickets or tokens, e.g. Kerberos
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
  • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
  • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
  • H04L9/3236—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions
  • H04L9/3242—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions involving keyed hash functions, e.g. message authentication codes [MACs], CBC-MAC or HMAC

Definitions

• WO2017093736A1 discloses a process of altering an original data set by combining data anonymization and digital watermarking.
• the anonymisation of the original data set can be achieved using a tokenisation technique, where tokenised values are generated with a regular expression.
• the regular expression must be known at the extraction time of the watermark.
• the tokenisation technique used includes a central vault which can cause problems for customers who have high throughput needs, or a requirement to consistently tokenise values in remote locations.
• Figure 12 shows a diagram illustrating the output with the final token ordinal.
• Figure 14 shows a diagram illustrating the process of extracting a watermark.
• Figure 16 shows a diagram illustrating the process of extracting a watermark on a set of parallel hash array.
• Figure 19 shows a diagram illustrating the extraction token count requirements as the number of data releases grows.
• Figure 23 shows a plot of the normalized computation time for watermark embedding.
• Figure 24 shows a plot of the normalized computation time for watermark extraction.
• Figure 25 shows the ultimate outcome of a watermark extraction performed with a confidence level of 95%, as the number of tokens processed in the extraction increases.
• Figure 28 shows a diagram plotting false positive occurrence percentage for the same experiments.
• Figure 30 shows results of an experiment with two data release watermarks mixed together.
• Input space - a space of all possible inputs from a set of original data that might need to be tokenised.
• the input space may be described using a regular expression. For example, when tokenising credit card numbers, a simple input space definition might be “[0-9] ⁇ 16 ⁇ ” - 16 decimal digits (this example ignores the complication that not all prefixes are valid, and the Luhn digit check, etc).
• Data release - generally refers to any release of tokenised data to a particular recipient for a particular purpose.
• Each data release is therefore associated with its own digital watermark.
• the digital watermark may be a number or other ‘ID’ which is stored in a watermark registry alongside metadata.
• Metadata may include for example the one or more recipients allowed to receive the data release, the purpose or intended use of the data release, how long the one or more recipients are legally allowed to retain the data, with whom they are allowed to share the data.
• Previous watermarking technique allows the generation of these tokens to be controlled so that a pattern is embedded within them.
• This pattern can be varied for each data release and allows a unique identifier for the release to be embedded within and across the data itself.
• This identifier can be used as a pointer to an arbitrary store of metadata about the data release - the intended recipient and purpose of the release, its lineage including the privacy treatments that have been applied to it, the date by which the data must be deleted, etc.
• This embedded pattern is probabilistic and is extractable from a sample of the generated tokens rather than being reliant on any individual tokens, so that it is still extractable from a sufficiently large subset of a data release.
• Each set of individual Hash Array keys is generated using a scheme like HKDF (a simple key derivation function KDF based on HMAC message authentication code) that allows expansion of a single master key into many different derived keys (and the ability to efficiently obtain a specific key by providing the ‘ID’ of the key in the input key material).
• KDF simple key derivation function
• HMAC message authentication code a simple key derivation function based on HMAC message authentication code
• Hash Array • Key Rolling: if each new Hash Array has its own key, any single Hash Array key is only in use for as long as the data releases within it are open and active (though old key versions must be kept around for as long as we wish to be able to extract watermarks generated using them).
• the number of unique watermarks that can be embedded and the fraction of tokens rejected depend on the configuration parameters of the algorithm, which are:
• the data set that we are attempting to extract a watermark from may not be a clean collection of tokens with no watermark tokens: it may have been doctored through the addition of new synthetic rows; it may be a combination of outputs from several data releases; or it may be that the assumptions made about the data shape when assigning the watermark inputs were not perfectly correct). Since the watermark is embedded using a secret key, it is not possible to craft noise that will be overrepresented in any particular bin without access to this key (either directly or indirectly through the watermark extraction function), which we assume is not available to anyone trying to erase a watermark.
• Figure 18 shows three histograms of token counts within each bin for the case of a ‘pure’ watermark (18A), a watermark with noise ( 18B), and two mixed watermarks.
• the p-value is defined as the probability of obtaining results at least as extreme as the observed results when the null hypothesis is true. In our case, this is the probability of getting the observed number of hashes (or fewer) in the bin when the data doesn’t contain a watermark corresponding to the bin.
• the computed p-value must be less than or equal to the (Holm- Bonferroni corrected) significance level, thus the minimum number of tokens is the point where:
• Getting an explicit expression for n that satisfies the above expression may be challenging, and so an estimation function instead may perform a brute force search over n and Ho find the number of tokens that satisfies the above inequality.
• Figure 21 shows a diagram illustrating extraction token count requirements as the number of data releases grows.
• Figure 22 shows a plot of the tokens required to extract the watermark at 99.9% confidence for the first Hash Array instance (supporting up to 256 data releases) as a function of the percentage of input noise.
• Embedding a watermark requires no state to be stored in memory and so is unaffected.
• Figure 25 shows the ultimate outcome of a watermark extraction performed with a confidence level of 95%, as the number of tokens processed in the extraction increases.
• Each data point is the average of 10,000 experiments and shows the split of outcomes across three mutually exclusive possibilities: no results returned; only the correct data release returned; the correct data release and an incorrect data release returned (note that there is a theoretical fourth outcome - only incorrect data releases returned - but this never occurred).
• Figure 28 shows the false positive occurrence percentage for the same experiments (here a false positive is recorded whenever at least one erroneous data release was returned, regardless of whether the correct data release was also returned).
• the false positive rate is bounded by the supplied confidence level (and that it does not depend on the level of noise).
• the false positive rate is not shown in the graphs above but is bounded at 5% as expected.
• Watermark tokens are assigned or determined dynamically at run time to avoid having to brute force search the entire token space.

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• 2022
  • 2022-09-22 WO PCT/GB2022/052401 patent/WO2023047114A1/en not_active Ceased
  • 2022-09-22 AU AU2022353195A patent/AU2022353195A1/en active Pending
  • 2022-09-22 JP JP2024517449A patent/JP2024535885A/ja active Pending
  • 2022-09-22 US US18/693,056 patent/US20250005115A1/en active Pending
  • 2022-09-22 CA CA3231917A patent/CA3231917A1/en active Pending
  • 2022-09-22 EP EP22793782.8A patent/EP4405837A1/en active Pending

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