The present invention provides various aspects for processing multiple types of substrates within cleanspace fabricators or for processing multiple or single types of substrates in multiple types of cleanspace environments particularly to form hardware based encryption devices and hardware based encryption equipped communication devices and multi-chip modules such as chiplets. In some embodiments, a collocated composite cleanspace fabricator may be capable of processing semiconductor devices into integrated circuits and then performing assembly operations to result in product in packaged form. Customized smart devices, smart phones and touchscreen devices may be fabricated in examples of a cleanspace fabricator. The assembly processing may include steps to form hardware based encryption.

An in-vehicle speech interaction method and a device are provided. The method includes: obtaining user speech information; determining a user instruction based on the user speech information; determining, based on the user instruction, whether response content to the user instruction is privacy-related; and determining, based on whether the response content is privacy-related, whether to output the response content in a privacy protection mode, to protect privacy from being leaked.

In one embodiment, a method comprises: establishing, by a secure executable container executed by a network device, a two-way trusted relationship with a verified subscriber that enables secure user access by the verified subscriber to a secure peer-to-peer data network, including creating a secure public key and a first device identifier uniquely identifying the network device, for insertion into a first device identity container that provides secure identification of the network device in the secure peer-to-peer data network; obtaining a second device identity container for a second network device and comprising a second public key owned by the second network device and a second device identifier uniquely identifying the second network device; and establishing a corresponding two-way trusted relationship with the second network device based on executing a prescribed secure salutation protocol with the second network device based on the second public key and the second device identifier.

Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and computing entities for predictive data protection using a data protection policy determination machine learning model. In one embodiment, a method is provided comprising: processing a historical data corpus using the data protection policy determination machine learning model to generate a dynamic data protection policy update describing inferred data protection instructions; determining an attestation subset of the inferred data protection instructions by comparing the instructions and prior data protection instructions described by an existing data protection policy; for each inferred data protection instruction in the attestation subset, determining a per-instruction attestation determination based on end-user feedback; generating an updated data protection policy by updating the existing policy in accordance with each inferred instruction in the attestation subset whose per-instruction attestation determination describes an affirmative attestation determination; and performing the predictive data protection using the updated data protection policy.

A system for telemedicine diagnostics through remote sensing includes a computing device configured to initiate a communication interface between the computing device and a client device operated by a human subject, wherein the secure communication interface includes an audiovisual streaming protocol, receive, from at least a remote sensor at the human subject, a plurality of current physiological data, generate a clinical measurement approximation as a function of the change of a first discrete and a second discrete set of current physiological data, wherein generating further comprises receiving approximation training data correlating physiological data with clinical measurement data, training a measurement approximation model as a function of the training data and a machine-learning process, and generating the clinical measurement approximation as a function of the current physiological data and the measurement approximation model, and presenting the clinical measurement approximation to a user of the computing device using the secure communication interface.

A secure guest of a computing environment requests confidential data. The confidential data is included in metadata of the secure guest, which is stored in a trusted execution environment of the computing environment. Based on the request, the confidential data is obtained from the metadata of the secure guest that is stored in the trusted execution environment.

A network communications method utilizing a network watermark for providing security in the communications includes creating a verifiable network communications path of nodes through a network for the transfer of information from a first end node to a second end node; verifying the network communications path of nodes, by the first end node, before communicating by the first end node information intended for receipt by the second end node; and once the network communications path of nodes is verified by the first end node, communicating by the first end node, via the verified communications path of nodes, the information intended for receipt by the second end node; wherein the network watermark represents the verifiable network communications path of nodes.

Described is an open communication system. The system includes a server having a memory storing user data and a first user computing device coupled to the server. The server may be programmed to allow multiple user computing devices to connect to the server and the server determines if the user computing devices are within a predetermined proximity to each other and whether the same communication channel is selected. All of the user computing devices that have selected the same communication channel and are within the predetermined proximity to each may be connected in an open communication link that allows the connected user to communicate. The system may include the option of establishing and invite particular users to a private or less used channel.

A financial institution computing system associated with a financial institution includes a network interface configured to communicate data over a network, and a processing circuit comprising a memory and a processor. The memory has instructions stored thereon that cause the processor to receive, by the network interface, a content request from a user computing device associated with a user, the content request requesting content from a network destination, determine if the network destination is associated with a trusted entity, determine that the requested content prompts the user to input sensitive information, and transmit, by the network interface substitution content to the user computing device responsive to determining that the network destination is illegitimate and to determining that the requested content includes at least one field into which the user may input sensitive information, the substitution content including at least one prompt requesting the user to input sensitive information.

A method for establishing a fully private, information secure interconnection between a source and a destination over a data network with at least a portion of a public infrastructure. The method comprising at the source creating n shares of a source data according to a predetermined secret sharing scheme, and encrypting the n shares using (n, k) secret sharing. Further, defining for at least one node vi a directed edge (Vi1, Vi2) that has a k−1 capacity. All outgoing links of vi are connected to vi2. Additionally, using a maximum flow algorithm to define the maximum number of shares outgoing from vi2, and therefore from vi, on each outgoing link. The number of shares forwarded by node vi does not exceed the number of maximum shares that were defined by the maximum flow algorithm.