Abstract: A client device encodes at least two datasets using one or more encoding functions to generate encoded data portions that are encrypted using a first key according to an approximate Fully Homomorphic Encryption (FHE) scheme to generate encrypted data portions that are sent to a plurality of servers. Encrypted results are received from at least a subset of servers of the plurality of servers. Each encrypted result is calculated by a respective server using at least two encrypted data portions received by the server. The encrypted results are decrypted using a secret key according to the approximate FHE scheme to derive decrypted encoded results that are decoded using an approximate decoding function. In one aspect, an encrypted result is calculated by each server by evaluating a multivariate function using the at least two encrypted data portions received by the server.

Abstract: Various processes for efficiently and effectively managing huge pages include a process for optimizing memory deduplication of huge pages, optimizing the promotion of one or more base pages to one or more huge pages and optimizing memory compaction of a memory space associated with a huge page.

Abstract: Various processes for efficiently and effectively managing huge pages include a process for optimizing memory deduplication of huge pages, optimizing the promotion of one or more base pages to one or more huge pages and optimizing memory compaction of a memory space associated with a huge page.

Abstract: System, methods, and devices described herein utilize blockchain systems to tie physical storage devices to a digital non-fungible token (NFT). By utilizing this association, the manufacturer can provide software to assure purchasers that their devices are genuine. Furthermore, ownership transfers can be verified and utilized as a means to provide accurate provenance of a storage device, which can increase the value of the device in certain collectible situations. Associating a storage device to an NFT can include pair the drive with a unique identifier provided by the manufacturer, and an address that consists of an encryption key and a secret key that only the owner will know. A user can then encrypt their data and transfer ownership of the storage device to a new owner who will generate a new encryption key. The new owner cannot access data stored on the device, preventing the original owner’s data from being compromised.

Abstract: Blockchain systems operate over a network of computing devices. Proof-of-space blockchain consensus systems utilize data (called plots) stored in storage devices across the computing devices within the network. These storage devices are utilized to generate and store proof-of-space consensus data. This data is then accessed at a later time to respond to challenges issued across the blockchain network. The owner of a plot may wish to sell a plot to another miner. If the seller is a bad-faith actor, they may retain copies of the secret key(s) and use them to continue mining the plot along with the buyer. To prevent these attacks, it may be desirable to submit a challenge response block where the proof-of-space is not visible as part of the challenge response. This may be done by replacing the proof-of-space with a proof-of-knowledge.

Abstract: Systems, methods, and devices described herein can configure a cryptocurrency wallet or other blockchain-based account to be self-aware and alert the owner of the wallet to one or more potentially fraudulent situations occurring with at least one account they own. The wallet may be a hardware-based cryptocurrency wallet or may be a blockchain-based account operating by an external financial institution that allows for the management of cryptocurrency assets. The self-aware wallet can be configured to track all relevant previously known and/or approved transactions associated with a user's private cryptocurrency key. A subsequent scan on one or more blockchains is performed to detect new transactions associated with the user's private key. If a newly detected blockchain transaction is not in the list of previously known or approved user transactions, a potential compromise may be occurring. Once detected, the wallet can generate a notification to the user alerting them to the issue.

Abstract: Systems, methods, and devices described herein achieve a more efficient plotting method for proof of space cryptocurrency processes, such as the Chia cryptocurrency system. Storage devices that are configured for use with proof of space cryptocurrency processing can be configured to utilize less space within a memory array during the initial farming or plotting process. This can be done by engaging in one or more data redundancy processes during the forward propagation step of table generation. Furthermore, additional efficiency can be achieved by using a modified backward propagation method during plotting that looks back fewer steps than traditional methods. Finally, additional efficiency can be achieved by utilizing modified compression methods as well as changing the configurations of the park settings within the tables. By utilizing these modified techniques and tuning them for increased efficiency, the amount of space and resources needed to generate new cryptocurrency plots can be reduced.

Abstract: Certain aspects of the present disclosure provide a method for performing multimode inferencing, comprising: receiving machine learning model input data from a requestor; processing the machine learning model input data with a machine learning model using processing hardware at a first power level to generate first output data; selecting a second power level for the processing hardware based on comparing the first output data to a threshold value; processing the machine learning model input data with the machine learning model using the processing hardware at the second power level to generate second output data; and sending second output data to the requestor.

Abstract: Blockchain systems operate over a network of computing devices. Proof of space blockchain consensus systems utilize data stored in storage devices across the computing devices within the network. These storage devices are utilized to generate and store proof of space consensus data. This data is then accessed at a later time to respond to challenges issued across the blockchain network. In order to limit successful submissions of these challenge responses, one or more filters are utilized. These filters result in only a fraction of the stored data on a storage device to be useable for solving the blockchain challenge. Attackers may attempt to circumvent this filter to increase their odds of submitting an approved solution to the blockchain challenge. In order to address this, additional data structures are stored within the storage device and are registered at the time of creation on the blockchain to make these filters more robust.

Abstract: Certain aspects of the present disclosure provide techniques for proving possession of data in a storage device participating in a distributed data storage network. An example storage device includes a storage circuitry and a trusted circuit. The storage circuitry is configured to store a plurality of data blocks. The trusted circuit generally has a private signing key securely stored thereon. The trusted circuit is generally configured to compute a hash over data stored in a plurality of data blocks and to generate an anonymous digital signature for the data stored in the plurality of data blocks based at least in part on the private signing key and the computed hash. The trusted circuit may be interposed on a write path to the storage circuitry such that data written to the storage circuitry is processed through the trusted circuit.

Abstract: Disclosed herein is an apparatus for estimating randomness of a random number generator. The apparatus is configured to divide output data (302), generated by the random number generator (704), into blocks (310) of a length (L), estimate a Shannon entropy of a second sub-set (404) of the blocks (310), using a first sub-set (402) of the blocks (310) to initialize the estimating, solve an estimate function, that relates an argument parameter (?) to the Shannon entropy estimate, to determine a value for the argument parameter (?) that is indicative of a probability of a most probable block being generated by the random number generator (704) as a new block, and use the length (L) to tune an estimate of randomness of the random number generator (704) calculated based on the value for the argument parameter (?).

Abstract: A system and method for allocating computational resources includes a plurality of classifiers, a memory array, and a memory controller to allocate memory from the memory array to each of the plurality of classifier. The system and method also include an optimization processor to determine an optimized bit precision value for at least one of the plurality of classifiers based upon a relative importance of the plurality of classifiers. The memory controller allocates the memory from the memory array to the plurality of classifiers based upon the determined optimized bit precision value.

Abstract: An apparatus is provided that includes a memory device including a plurality of sub-arrays, and a memory controller. The memory controller is configured to determine a value of a parameter of a corresponding write pulse for each bit of a word based on a relative importance of each bit, and write each bit of the word to a corresponding one of the plurality of sub-arrays using the corresponding write pulses.

Abstract: Certain aspects of the present disclosure provide techniques for performing machine learning, including: processing a training data instance with a task model to generate an encoding and a task model output; processing a discriminator input based on the encoding using a discriminator model to generate an estimated mutual information between the encoding and the one or more input variables of the training data instance; updating parameters of the discriminator model using a first iterative optimization algorithm to maximize a discriminator objective function based on the estimated mutual information; and updating parameters of the task model using a second iterative optimization algorithm to minimize a task objective function based on a sum of the estimated mutual information between the task model output and the one or more input variables of the training data instance and a conditional entropy between the target variable and an encoding generated by the task model.

Abstract: Modifying a page stored in a non-volatile storage includes receiving one or more requests to modify data stored in the page with new data. One or more lines are identified in the page that include data to be modified by the one or more requests. The identified one or more lines correspond to one or more respective byte ranges each of a predetermined size in the page. Encoded data is created based on the new data and respective locations of the one or more identified lines in the page. The encoded data is cached, and at least a portion of the cached encoded data is used to rewrite the page in the non-volatile storage to include at least a portion of the new data.

Abstract: Disclosed herein is an apparatus for estimating randomness of a random number generator 704. The apparatus is configured to divide output data 302, generated by the random number generator 704, into blocks of a length (L), estimate a collision entropy of the blocks 310, solve a closed-form estimate function, that relates an argument parameter (?(2)) to the estimated collision entropy, to determine a value for the argument parameter (?(2)) that is indicative of a probability of a most probable block being generated by the random number generator 704 as a new block, and use the length (L) to tune an estimate of randomness of the random number generator 704 calculated based on the value for the argument parameter (?(2)).

Abstract: Disclosed herein is an apparatus for estimating randomness of a random number generator. The apparatus is configured to divide output data (302), generated by the random number generator (704), into blocks (310) of a length (L), estimate a Shannon entropy of a second sub-set (404) of the blocks (310), using a first sub-set (402) of the blocks (310) to initialize the estimating, solve an estimate function, that relates an argument parameter (?) to the Shannon entropy estimate, to determine a value for the argument parameter (?) that is indicative of a probability of a most probable block being generated by the random number generator (704) as a new block, and use the length (L) to tune an estimate of randomness of the random number generator (704) calculated based on the value for the argument parameter (?).

Abstract: Modifying a page stored in a non-volatile storage includes receiving one or more requests to modify data stored in the page with new data. One or more lines are identified in the page that include data to be modified by the one or more requests. The identified one or more lines correspond to one or more respective byte ranges each of a predetermined size in the page. Encoded data is created based on the new data and respective locations of the one or more identified lines in the page. The encoded data is cached, and at least a portion of the cached encoded data is used to rewrite the page in the non-volatile storage to include at least a portion of the new data.

Abstract: Certain aspects of the present disclosure provide a method for performing multimode inferencing, comprising: receiving machine learning model input data from a requestor; processing the machine learning model input data with a machine learning model using processing hardware at a first power level to generate first output data; selecting a second power level for the processing hardware based on comparing the first output data to a threshold value; processing the machine learning model input data with the machine learning model using the processing hardware at the second power level to generate second output data; and sending second output data to the requestor.

Abstract: An apparatus is provided that includes a memory device including a plurality of sub-arrays, and a memory controller. The memory controller is configured to determine a value of a parameter of a corresponding write pulse for each bit of a word based on a relative importance of each bit, and write each bit of the word to a corresponding one of the plurality of sub-arrays using the corresponding write pulses.