Machine learning model-based content anonymization
A system includes a computing platform having processing hardware, and a system memory storing software code and a machine learning (ML) model. The processing hardware is configured to execute the software code to receive from a client, a request for a dataset, the request identifying a content type of the dataset, obtain the dataset having the content type, and select, based on the content type, an anonymization technique for the dataset, the anonymization technique selected so as to render at least one feature included in the dataset recognizable but unidentifiable. The processing hardware is further configured to execute the software code to anonymize, using the ML model and the selected anonymization technique, the at least one feature included in the dataset, and to output to the client, in response to the request, an anonymized dataset including the at least one anonymized feature.
1. A system comprising:
a processing hardware; and
a system memory storing a software code, a first trained neural network (NN) and a second trained NN;
the processing hardware configured to execute the software code to:
receive from a client, a request for a dataset, the request identifying a content type of the dataset, the dataset including an image;
obtain the dataset having the content type;
select, based on the content type, an anonymization technique for the dataset, the anonymization technique selected so as to render at least one feature included in the dataset recognizable but unidentifiable;
anonymize, using the first trained NN and the selected anonymization technique, the at least one feature included in the image, such that a generic nature of the at least one feature included in the image is maintained by anonymizing, but an identifiable nature of the at least one feature included in the image is removed by anonymizing, wherein the at least one feature included in the image and anonymized is indicative of at least one of a location where the image is captured or an activity being performed, wherein the activity includes one of dancing, fencing, first bumping, hand-to-hand fighting, hand clapping, handshaking, hand-fiving, holding hands, hugging, performing magic, or pushing;
evaluate, using the second trained NN, an anonymity of the at least one anonymized feature;
re-anonymize, using the first trained NN, the at least one anonymized feature when evaluating the anonymity of the at least one anonymized feature fails to confirm that the at least one anonymized feature is unidentifiable, until when evaluating the anonymity of the at least one anonymized feature indicates that a confidence value of the at least one anonymized feature being unidentifiable satisfies a predetermined threshold; and
output to the client, in response to the request, an anonymized dataset including the image having the at least one anonymized feature.
2. The system of claim 1 , wherein the at least one feature included in the image and anonymized is indicative of the location where the image is captured.
3. The system of claim 1 , wherein prior to obtaining the dataset having the content type, the processing hardware is further configured to execute the software code to:
determine that quota for content having the content type does not exceed an allowable limit; and
in response to determining, including the content in the dataset having the content type.
4. The system of claim 1 , wherein the image is in a video frame.
5. The system of claim 1 , wherein the at least one feature included in the image further comprises a depiction of a person or a character, and the at least one feature and anonymized is indicative of the location where the image is captured and includes one of a building, a landmark or a restaurant.
6. The system of claim 1 , wherein the content type comprises digital representations that populate a virtual reality, augmented reality, or mixed reality environment.
7. A method for use by a system including a processing hardware, and a system memory storing a software code, a first trained neural network (NN) and a second trained NN, the method comprising:
receiving from a client, by the software code executed by the processing hardware, a request for a dataset, the request identifying a content type of the dataset, the dataset including an image;
determining, by the software code executed by the processing hardware, quota for content having the content type does not exceed an allowable limit;
obtaining, by the software code executed by the processing hardware, the dataset having the content type and including the content;
selecting, by the software code executed by the processing hardware, based on the content type, an anonymization technique for the dataset, the anonymization technique selected so as to render at least one feature included in the dataset recognizable but unidentifiable;
anonymizing, by the software code executed by the processing hardware and using the first trained NN and the selected anonymization technique, the at least one feature included in the image, such that a generic nature of the at least one feature included in the image is maintained by anonymizing, but an identifiable nature of the at least one feature included in the image is removed by anonymizing, wherein the at least one feature included in the image and anonymized is indicative of at least one of a location where the image is captured or an activity being performed, wherein the activity includes one of dancing, fencing, first bumping, hand-to-hand fighting, hand clapping, handshaking, hand-fiving, holding hands, hugging, performing magic, or pushing;
evaluate, by the software code executed by the processing hardware and using the second trained NN, an anonymity of the at least one anonymized feature;
re-anonymize, by the software code executed by the processing hardware and using the first trained NN, the at least one anonymized feature when evaluating the anonymity of the at least one anonymized feature fails to confirm that the at least one anonymized feature is unidentifiable, until when evaluating the anonymity of the at least one anonymized feature indicates that a confidence value of the at least one anonymized feature being unidentifiable satisfies a predetermined threshold; and
outputting to the client, by the software code executed by the processing hardware in response to the request, an anonymized dataset including the image having the at least one anonymized feature.
8. The method of claim 7 , wherein the at least one feature included in the image and anonymized is indicative of the location where the image is captured.
9. The method of claim 7 , wherein prior to obtaining the dataset having the content type, the method further comprises:
determining, by the software code executed by the processing hardware, that quota for content having the content type does not exceed an allowable limit; and
in response to determining, including, by the software code executed by the processing hardware, the content in the dataset having the content type.
10. The method of claim 7 , wherein the image is in a video frame.
11. The method of claim 7 , wherein the at least one feature included in the image further comprises a depiction of a person or a character, and the at least one feature and anonymized is indicative of the location where the image is captured and includes one of a building, a landmark or a restaurant.
12. The method of claim 7 , wherein the content type comprises digital representations that populate a virtual reality, augmented reality, or mixed reality environment.