Proactive privacy content hosting

1. A method comprising:

identifying, by a first computer system configured to host media items for various users, a first and second set of media items;

determining to skip altering media items of the second set of media items in response to the second set of media items not having a relationship that indicates a shared origin;

identifying that media items of the first set of media items have a first relationship;

performing, based on a first machine-learning model and in response to the first set of media items having the first relationship, a content analysis on the first set of media items;

determining, based on the content analysis, a first content pattern contained within the first set of media items;

detecting a first data-access request from a second computer system sent during a computing session, where the first data-access request is directed to the first set of media items;

determining a first purpose of the first data-access request by comparing data of the computing session and the second computer system against a corpus that contains historical data access requests and purpose determinations for computing session factors; and

generating, in response to the first content pattern, a first set of one or more altered media items that is altered responsive to the first purpose.

2. The method of claim 1 further comprising:

determining, based on the content analysis, a second content pattern contained within the first set of media items; and

generating, in response to the second content pattern, a second set of one or more altered media items.

3. The method of claim 1 further comprising:

associating the first set of altered media items with first the set of media items; and

storing, in a data store, a plurality of sets of altered media items including the first set of altered media items.

4. The method of claim 1 , wherein the generating the first set of altered media items further comprises:

creating a third set of media items, wherein each media item in the third set of media items is a copy of each media item in the first set of media items; and

altering, based on the first content pattern, each media item in the third set of media items.

5. The method of claim 1 , wherein

the first set of media items includes a video,

the video includes a plurality of video images, and

the altering includes altering one or more content objects in a subset of the plurality of video images.

6. The method of claim 1 , wherein

the first set of media items includes a static image, and

the altering includes altering one or more content objects in the static image.

7. The method of claim 1 , wherein

the first set of media items includes a textual string, and

the altering includes altering one or more content objects in the textual string.

8. The method of claim 1 , wherein the first machine-learning model is based on a knowledge corpus that includes the corpus related to data access requests, and wherein the method further comprises:

creating the knowledge corpus from user information related to the first set of media items.

9. The method of claim 8 further comprising:

updating the knowledge corpus based on a received data-access request.

10. The method of claim 1 , wherein the first relationship is the first set of media items are received from the same location.

11. The method of claim 1 , wherein the first relationship is the first set of media items are received from the same user.

12. The method of claim 1 , wherein the first relationship is the first set of media items are received from the same user device.

13. The method of claim 1 , wherein the first relationship is a time of receipt where the first set of media items are received during the same time period.

14. A system, the system comprising:

a memory, the memory containing one or more instructions; and

a processor, the processor communicatively coupled to the memory, the processor, in response to reading the one or more instructions, configured to:

identify, by a first computer system configured to host media items for various users, a first and second set of media items;

determine to skip altering media items of the second set of media items in response to the second set of media items not having a relationship that indicates a shared origin;

identify that media items of the first set of media items have a first relationship;

perform, based on a first machine-learning model and in response to the first set of media items having the first relationship, a content analysis on the first set of media items;

determine, based on the content analysis, a first content pattern contained within the first set of media items;

detect a first data-access request from a second computer system sent during a computing session, where the first data-access request is directed to the first set of media items;

determine a first purpose of the first data-access request by comparing data of the computing session and the second computer system against a corpus that contains historical data access requests and purpose determinations for computing session factors; and

generate, in response to the first content pattern, a first set of one or more altered media items that is altered responsive to the first purpose.

15. The system of claim 14 , wherein the first machine-learning model is based on a knowledge corpus that includes the corpus related to data access requests, and wherein the processor is further configured to:

create the knowledge corpus from user information related to the first set of media items.

16. A computer program product, the computer program product comprising:

one or more computer readable storage media; and

program instructions collectively stored on the one or more computer readable storage media, the program instructions configured to:

identify, by a first computer system configured to host media items for various users, a first and second set of media items;

determine to skip altering media items of the second set of media items in response to the second set of media items not having a relationship that indicates a shared origin;

identify that media items of the first set of media items have a first relationship;

perform, based on a first machine-learning model and in response to the first set of media items having the first relationship, a content analysis on the first set of media items;

determine, based on the content analysis, a first content pattern contained within the first set of media items;

detect a first data-access request from a second computer system sent during a computing session, where the first data-access request is directed to the first set of media items;

determine a first purpose of the first data-access request by comparing data of the computing session and the second computer system against a corpus that contains historical data access requests and purpose determinations for computing session factors; and

generate, in response to the first content pattern, a first set of one or more altered media items that is altered responsive to the first purpose.