# US 12,010,128 B2 · Methods, systems, articles of manufacture and apparatus to build privacy preserving models

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- Number: US 12,010,128 B2
- Document ID: US12010128B2
- Status: Granted
- Kind: B2
- Application number: 17/125,364
- Filed: 2020-12-17
- Published: 2022-06-23
- Granted: 2024-06-11
- Earliest claimed: 2020-12-17
- Assignees: McAfee, Inc.
- Applicants: McAfee, LLC
- Inventors: Piyush P. Joshi; Abhishek Tripathi; Tirumaleswar Reddy Konda
- Web page: https://patentgrep.com/patent/US12010128B2

## CPC

- H04L 63/1425: {Traffic logging, e.g. anomaly detection}
- G06F 18/2148: {characterised by the process organisation or structure, e.g. boosting cascade}
- G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques}
- H04L 63/1416: {Event detection, e.g. attack signature detection}
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Methods, apparatus, systems and articles of manufacture are disclosed to build privacy preserving models. An example apparatus disclosed herein includes a training manager to generate a first modeling plan for client-side resources, and transmit the first modeling plan to the client-side resources. The example apparatus also includes a data aggregator to search for a primary validation flag in response to retrieving client-side model parameters, and an accuracy calculator to, in response to detecting the primary validation flag, perform a secondary validation corresponding to the client-side model parameters using a server-side ground truth data set, and determine whether to update the global model with the client-side model parameters based on a comparison of results of the secondary validation and a validation threshold.

## Claims

### Claim 1

A computer system to update a global model, comprising:
memory;
machine readable instructions; and
processor circuitry to at least one of instantiate or execute the machine readable instructions to:
generate a first modeling plan for client-side resources, the first modeling plan including (a) tokenized model parameters devoid of telemetry labels and (b) a transmission schedule for the client-side resources;
transmit the first modeling plan to the client-side resources;
search for a primary validation flag in response to retrieving client-side model parameters;
in response to detecting the primary validation flag, perform a secondary validation corresponding to the client-side model parameters using a server-side ground truth data set, the secondary validation based on calculating a confidence metric in view of the server-side ground truth data set and determining that the confidence metric satisfies a validation threshold;
authorize updating of the global model in response to results of the secondary validation satisfying the validation threshold;
in response to the confidence metric not satisfying the validation threshold, prohibit updating the global model with the client-side model parameters;
send a second modeling plan to the client-side resources in response to rejecting the client-side modeling parameters; and
cause the client-side resources to execute the second modeling plan instead of the first modeling plan.

### Claim 2 (depends on 1)

The computer system as defined in claim 1, wherein the transmission schedule includes at least one of a target epoch, a quantity of training dataset iterations, a periodicity of communication with a server for a next training iteration, or a quantity of training samples to be processed by the client-side resources.

### Claim 3 (depends on 1)

The computer system as defined in claim 1, wherein the global model is to cause the client-side resources to at least one of identify computing device types or perform anomaly detection.

### Claim 4

At least one non-transitory machine readable storage medium comprising instructions that cause a processor to at least:
generate a first modeling plan for client-side resources, the first modeling plan including (a) tokenized model parameters devoid of telemetry labels and (b) a transmission schedule for the client-side resources;
transmit the first modeling plan to the client-side resources;
search for a primary validation flag in response to retrieving client-side model parameters;
in response to detecting the primary validation flag, perform a secondary validation corresponding to the client-side model parameters using a server-side ground truth data set, the secondary validation based on calculating a confidence metric corresponding to the server-side ground truth data set and determining that the confidence metric satisfies a validation threshold;
authorize updating of a global model in response to results of the secondary validation satisfying the validation threshold;
in response to the confidence metric not satisfying the validation threshold, prohibit updating the global model with the client-side model parameters;
send a second modeling plan to the client-side resources in response to rejecting the client-side modeling parameters; and
cause the client-side resources to execute the second modeling plan instead of the first modeling plan.

### Claim 5 (depends on 4)

The at least one non-transitory machine readable storage medium as defined in claim 4, wherein the transmission schedule includes at least one of a target epoch, a quantity of training dataset iterations, or a quantity of training samples to be processed by the client-side resources.

### Claim 6 (depends on 4)

The at least one non-transitory machine readable storage medium as defined in claim 4, wherein the instructions cause the processor to cause the client-side resources to at least one of identify computing device types or perform anomaly detection.

### Claim 7

A method to update a global model, comprising:
generating a first modeling plan for client-side resources, the first modeling plan including (a) tokenized model parameters devoid of telemetry labels and (b) a transmission schedule for the client-side resources;
transmitting the first modeling plan to the client-side resources;
searching for a primary validation flag in response to retrieving client-side model parameters;
in response to detecting the primary validation flag, performing a secondary validation corresponding to the client-side model parameters using a server-side ground truth data set, the secondary validation based on calculating a confidence metric in view of the server-side ground truth data set;
authorize updating of the global model in response to results of the secondary validation satisfying a validation threshold;
in response to determining that the confidence metric does not satisfy a validation threshold prohibit updating the global model with the client-side model parameters;
sending a second modeling plan to the client-side resources in response to rejecting the client-side modeling parameters; and
causing the client-side resources to execute the second modeling plan instead of the first modeling plan.

### Claim 8 (depends on 7)

The method as defined in claim 7, wherein the transmission schedule includes at least one of a target epoch, a quantity of training dataset iterations, or a quantity of training samples to be processed by the client-side resources.

### Claim 9 (depends on 7)

The method as defined in claim 7, further including causing the client-side resources to at least one of identify computing device types or perform anomaly detection.