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Of the 19 tenant attributes, 8 are sensitive, such as the lists of patients.
Policy federation effectively succeeds in keeping the sensitive tenant attributes and policies confidential.
This is caused by the fact that the policies from the case study require more tenant attributes than provider attributes.
The policies from the case study all require more tenant attributes than provider attributes, except for P9.
The cost functions determine the cost of the provider (CAtom,P) and the tenant (CAtom,T) evaluating a certain atomic policy based on the total number of required provider attributes (NA,P) and tenant attributes (NA,T) and the cost for fetching an attribute locally (C L ) or remotely (C R ).
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The two six-story buildings on Ocean Avenue are slated for demolition, and the tenants attribute their displacement to a wave of development that has swept the shore town the last few years.
For the latter, we argue that the possibly inferred knowledge is limited since both the tenant policies and the required attributes remain confidential and the provider can only request the tenant to evaluate the policies resulting from the federation algorithm.
The goal of the policy federation algorithm is to decompose and distribute the tenant policies so that sensitive attributes and policies remain confidential and the evaluation performance is optimized, i.e., the evaluation duration is minimized.
Request 13 is the most extreme case, where all required attributes are stored tenant-side and 7 attribute requests are replaced by a single policy evaluation request.
The latency of a remote attribute fetch between tenant and provider will be an order of magnitude larger than a local database call, taking into account the complex data flows in federated applications and the geographical distance between tenant and provider.
The same number is achieved if P9 (i.e., the part of the policy tree that is deployed provider-side) is not required to reach an access control decision, e.g., for requests 13 to 16. Smaller numbers are achieved in the other cases, e.g., requests 4 to 7. In these cases, multiple attribute fetches from tenant to provider are replaced by a single policy evaluation request.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com