Abstract: An example operation may include one or more of storing a broadcast encryption tree comprising a set of cryptographic keys disposed in a hierarchical tree format, distributing a partial set of keys from the broadcast encryption tree to each respective peer from among a group of peers included in a blockchain network, receiving, from a user device, an identification of at least one peer included in the group of peers for processing a blockchain transaction, selecting a subset of keys from among the set of cryptographic keys in the broadcast encryption tree which enables at least one peer to decrypt transactions and doesn't enable the remaining peers included in the group of peers to decrypt transactions, and transmitting broadcast encryption information about the selected subset of keys to the user device for performing encryption of the transactions.

Abstract: An example operation may include one or more of storing a broadcast encryption tree comprising a set of cryptographic keys disposed in a hierarchical tree format, distributing a partial set of keys from the broadcast encryption tree to each respective peer from among a group of peers included in a blockchain network, receiving, from a user device, an identification of at least one peer included in the group of peers for processing a blockchain transaction, selecting a subset of keys from among the set of cryptographic keys in the broadcast encryption tree which enables at least one peer to decrypt transactions and doesn't enable the remaining peers included in the group of peers to decrypt transactions, and transmitting broadcast encryption information about the selected subset of keys to the user device for performing encryption of the transactions.

Abstract: An example operation may include one or more of storing a broadcast encryption tree comprising a set of cryptographic keys disposed in a hierarchical tree format, distributing a partial set of keys from the broadcast encryption tree to each respective peer from among a group of peers included in a blockchain network, receiving, from a user device, an identification of at least one peer included in the group of peers for processing a blockchain transaction, selecting a subset of keys from among the set of cryptographic keys in the broadcast encryption tree which enables at least one peer to decrypt transactions and doesn't enable the remaining peers included in the group of peers to decrypt transactions, and transmitting broadcast encryption information about the selected subset of keys to the user device for performing encryption of the transactions.

Abstract: An example operation may include one or more of storing a broadcast encryption tree comprising a set of cryptographic keys disposed in a hierarchical tree format, distributing a partial set of keys from the broadcast encryption tree to each respective peer from among a group of peers included in a blockchain network, receiving, from a user device, an identification of at least one peer included in the group of peers for processing a blockchain transaction, selecting a subset of keys from among the set of cryptographic keys in the broadcast encryption tree which enables at least one peer to decrypt transactions and doesn't enable the remaining peers included in the group of peers to decrypt transactions, and transmitting broadcast encryption information about the selected subset of keys to the user device for performing encryption of the transactions

Abstract: Method and system aspects for searching for an optimal codevector from a plurality of codevectors in a codebook, the optimal codevector having a minimum distance to a given vector, are provided. The aspects determine a partial distance with a current vector component of a current codevector and of the given vector, compare the partial distance to a saved renormalized minimum partial distance, and proceed to a next codevector when the saved renormalized minimum partial distance is smaller than the partial distance. In addition, the present invention proceeds to a next vector component when the partial distance is smaller than the saved renormalized minimum partial distance. When the partial distance computed with each next vector component is smaller than the saved renormalized minimum partial distance, the present invention calculates a full weighted distance value, compares it to a saved minimum full weighted distance, and updates chosen values.

Abstract: A communication system encodes input data by comparing the data to records in a lossy codebook to determine the closest match. An index corresponding to the record that most closely matches the input data is retrieved from the codebook and transmitted to a receiver. For a predetermined sampling interval, the communication system collects statistics of the frequency of occurrence of each record matching the input data. The records are sorted in descending order by the frequency of occurrence. Using either a predetermined error rate or a predetermined subtable size, the communication system creates a subtable of the records having the greatest frequency of occurrence. To reduce the search time of finding a record matching the input data, the communication system searches only the subtable for records until the error of the match in the subtable exceeds a preselected threshold or until a preselected period of time has elapsed. The communication system again collects statistics of the frequency of occurrence.

Abstract: A method for improved recursive pitch prediction includes providing a search window for pitch estimates based upon a previously computed pitch, computing pitch estimates for the search window, and determining an optimal pitch from the pitch estimates within the search window for a first predetermined number of frames. The method further includes expanding the search window to a full pitch window after the first predetermined number of frames, and calculating pitch estimates for the full pitch window for a second predetermined number of frames.A system for improved recursive pitch prediction includes a speech generator of speech signals, and a central processing unit coupled to the speech generator.

Abstract: Method and system aspects for linear predictive speech encoding are disclosed. These aspects comprise the definition of an error function, the computation of an optimal vector of continuous pitch coefficients together with an optimal pitch, and the weighted vector quantization of the continuous pitch coefficients. The techniques allows the faster computation of the optimal combination pitch--continuous coefficient values without substantial loss of optimal results.