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US-20260126918-A1 - DATA TRANSMISSION MANAGEMENT

US20260126918A1US 20260126918 A1US20260126918 A1US 20260126918A1US-20260126918-A1

Abstract

Methods, apparatuses, and non-transitory machine-readable media associated with data transmission are described. Data transmission management can include receiving, from an edge device via a radio at a first device, instructions associated with data transmission between a second device in communication with the first device and a cloud service in communication with the first device. Data transmission management can also include managing, at the first device and based on the instructions from the edge device, data received from a memory resource of the second device for transmission to the cloud service and data received from the cloud service for transmission to the memory resource of the second device. Data transmission management can further include enabling transmission of some, none, or all of the data between the cloud service and the memory resource of the second device and vice versa based on the management of the data.

Inventors

  • Fatma Arzum Simsek-Ege
  • Carly M. Wantulok
  • Sumana Adusumilli
  • Chiara Cerafogli

Assignees

  • MICRON TECHNOLOGY, INC.

Dates

Publication Date
20260507
Application Date
20251219

Claims (20)

  1. 1 . A device, comprising: a processing resource; and a memory resource having instructions written thereon and executable by the processing resource to: receive, via a radio, first input data from an edge device; receive, via the radio, second input data from a cloud service; receive, via the radio, third input data from a system-on-a-chip (SoC) device; determine a sensitivity level of the second input data and a sensitivity level of the third input data using the first input data and based on a comparison of the second input data and the third input data to a database; allow transmission of the second input data to the SoC device responsive to the sensitivity level of the second input data being below a particular threshold; allow transmission of the third input data to the cloud service responsive to the sensitivity level of the third input data being below the particular threshold; prohibit transmission of the second input data to the SoC device and write the second input data to the memory resource or a buffer memory resource of the device responsive to the sensitivity level of the second input data being at or above the particular threshold; and prohibit transmission of the third input data to the cloud service and write the third input data to the memory resource or a buffer memory resource of the device responsive to the sensitivity level of the third input data being at or above the particular threshold.
  2. 2 . The device of claim 1 , further comprising the memory resource having instructions written thereon and executable by the processing resource to enable transmission of additional data between the cloud service and the SoC device to facilitate supervised machine learning at the SoC device.
  3. 3 . The device of claim 1 , further comprising the memory resource having instructions written thereon and executable by the processing resource to maintain cache coherence between the device, the SoC device, and the cloud service excepting the second and the third input data having sensitivity levels at or above the particular threshold.
  4. 4 . The device of claim 1 , wherein the particular threshold is based on sensitivity level threshold data within the first input data.
  5. 5 . The device of claim 1 , further comprising a secure connection between the device and the SoC device including a switch.
  6. 6 . The device of claim 5 , wherein: allowing transmission of the second input data includes opening the switch and transmitting the second input data via the secure connection; and prohibiting transmission of the second input data includes closing the switch.
  7. 7 . The device of claim 1 , wherein determining a sensitivity level of the second data comprises, the device, in response to receiving an unknown term, sending a prompt to the edge device requesting a determination of whether the term should be allowed and receiving instructions from the edge device indicating whether to allow or prohibit transmission of the unknown term.
  8. 8 . The device of claim 7 , further comprising the memory resource having instructions written thereon and executable by the processor to, subsequent to receiving the instructions from the edge device indicating whether to allow or prohibit transmission of the unknown term and in response to receiving the unknown term again, allow or prohibit the unknown term based on the instructions.
  9. 9 . The device of claim 1 , wherein determining a sensitivity level of the third data comprises the device, in response to receiving data indicated by the second device to be potentially sensitive, sending a prompt to the edge device requesting a determination of whether the term should be allowed.
  10. 10 . A system, comprising: a cloud service; an edge device; a first device comprising: a processing resource; and a memory resource; and a second device; and the memory resource of the first device having instructions written thereon and executable by the processing resource of the first device to: receive first input data from the edge device; receive second input data from the cloud service; receive third input data from the second device; determine a sensitivity level of the second input data and a sensitivity level of the third input data using the first input data and based on a comparison of the second input data and the third input data to a database; allow or prohibit transmission of the second data to the second device based on the determined sensitivity level; and allow or prohibit transmission of the third data to the cloud service based on the determined sensitivity level.
  11. 11 . The system of claim 10 , wherein the second device is in communication with the first device via a switch that is part of a secure connection between the first device and the second device.
  12. 12 . The system of claim 11 , wherein the secure connection between the first device and the second device uses single-based error correction code (ECC) to enable reliable transmission of the data between the first device and the second device.
  13. 13 . The system of claim 12 , wherein the ECC is inserted on an integrated circuit of the second device and used as a signature to protect sensitive data.
  14. 14 . The system of claim 13 , wherein the secure connection between the first device and the second device includes a sensor located in the memory resource of the first device.
  15. 15 . A method, comprising: receiving, at a first device, first input data from an edge device; receiving, at the first device, second input data from a cloud service; receiving, at the first device, third input data from a second device; determining, using the first device, a sensitivity level of the second input data and a sensitivity level of the third input data using the first input data and based on a comparison of the second input data and the third input data to a database; using the first device, opening a switch to allow transmission of the second input data to the second device responsive to the sensitivity level of the second input data being below a particular threshold; using the first device, closing the switch to prohibit transmission of the second input data to the SoC device and write the second input data to the memory resource or a buffer memory resource of the device responsive to the sensitivity level of the second input data being at or above the particular threshold.
  16. 16 . The method of claim 15 , comprising receiving the third input data at the first device in response to the second device determining the content of an interaction with the second device using machine learning.
  17. 17 . The method of claim 16 , wherein the second device determining the content of an interaction comprises the second device receiving an input via a sensor of the second device.
  18. 18 . The method of claim 17 , wherein receiving an input via sensor comprises receiving verbal communication.
  19. 19 . The method of claim 17 , wherein receiving input via a sensor comprises receiving input via a touch screen.
  20. 20 . The method of claim 15 , further comprising disabling the second device responsive to the first device determining data received from the second device comprises at least a portion of data of unknown content.

Description

PRIORITY INFORMATION This Application is a Divisional Application of U.S. Application Serial No. 18/379,343 filed on October 12, 2023, which is a Divisional Application of U.S. Application Serial No. 17/236,183 filed on April 21, 2021, which issued as U.S. Patent No. 11,797,192 on October 24, 2023, the contents of which are incorporated herein by reference. TECHNICAL FIELD The present disclosure relates generally to apparatuses, non-transitory machine-readable media, and methods associated with data transmission management. BACKGROUND Memory resources are typically provided as internal, semiconductor, integrated circuits in computers or other electronic systems. There are many different types of memory, including volatile and non-volatile memory. Volatile memory can require power to maintain its data (e.g., host data, error data, etc.). Volatile memory can include random access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), synchronous dynamic random-access memory (SDRAM), and thyristor random access memory (TRAM), among other types. Non-volatile memory can provide persistent data by retaining stored data when not powered. Non-volatile memory can include NAND flash memory, NOR flash memory, and resistance variable memory, such as phase change random access memory (PCRAM) and resistive random-access memory (RRAM), ferroelectric random-access memory (FeRAM), and magnetoresistive random access memory (MRAM), such as spin torque transfer random access memory (STT RAM), among other types. Electronic systems often include a number of processing resources (e.g., one or more processing resources), which may retrieve instructions from a suitable location and execute the instructions and/or store results of the executed instructions to a suitable location (e.g., the memory resources). A processing resource can include a number of functional units such as arithmetic logic unit (ALU) circuitry, floating point unit (FPU) circuitry, and a combinatorial logic block, for example, which can be used to execute instructions by performing logical operations such as AND, OR, NOT, NAND, NOR, and XOR, and invert (e.g., NOT) logical operations on data (e.g., one or more operands). For example, functional unit circuitry may be used to perform arithmetic operations such as addition, subtraction, multiplication, and division on operands via a number of operations. Artificial intelligence (AI) can be used in conjunction memory resources. AI can include a controller, computing device, or other system to perform a task that normally requires human intelligence. AI can include the use of one or more machine learning models. As described herein, the term “machine learning” refers to a process by which a computing device is able to improve its own performance through iterations by continuously incorporating new data into an existing statistical model. Machine learning can facilitate automatic learning for computing devices without human intervention or assistance and adjust actions accordingly. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a functional diagram representing a system for data transmission management in accordance with a number of embodiments of the present disclosure. FIG. 2 is a system diagram including a first device in communication with a cloud service and a second device for data transmission management in accordance with a number of embodiments of the present disclosure. FIG. 3 is another functional diagram representing a processing resource in communication with a memory resource having instructions written thereon in accordance with a number of embodiments of the present disclosure. FIG. 4 is a flow diagram representing an example method for data transmission management in accordance with a number of embodiments of the present disclosure. DETAILED DESCRIPTION Systems, devices, and methods related to data transmission management are described. In a shared memory multiprocessor system with a separate cache memory for each processor, it may be possible to have many copies of shared data. For instance, there may be one copy in a main memory and one in a local cache of each processor that requested it. When one of the copies of data is changed, the other copies may reflect that change. Cache coherence ensures that the changes in the values of shared data are propagated throughout the system with a desired timeliness. However, cache coherence may result in sharing of sensitive data, for instance, as data is shared throughout the system. Examples of the present disclosure can allow for selective cache coherence such that some direct communication is enabled while sensitive data is isolated, for instance from a cloud service and/or public internet. Put another way, examples of the present disclosure allow for intentional splitting of cache coherence within the system using a gatekeeper-like device. Examples of the present disclosure can include a method for data transmission management i