From: icaps-conference.org/ICAPS/2019/Workshop/SPARK/Program_Chairs
title: Acceptance Decision
decision: Accept
comment: Excellent as a workshop paper

From: icaps-conference.org/ICAPS/2019/Workshop/SPARK/Paper9/AnonReviewer2
title: A very interesting ongoing work.
review: This paper considers the use of secure multi-party computation (MPC) when the members of a coalition is engaged in solving  an aid delivery scheduling problem (HADR Aid Delivery Problem) that involves coordinating  N Aid-Providers, each of which has some number of ships (with aid to be delivered) and a single Aid-Recipient, that has P ports to which aid can be delivered.  The objective is the maximization of the load-balancing across all ports, while still meeting some privacy requirements.

Basically, the paper gives a twofold contribution:
- one is about the use of a technology called quantitative information flow (QIF) to measure the amount of information about the private data used in the scheduling task might leak to other participants;
- a second contribution is about the automated adjustments of the scheduling process to ensure privacy requirements.

Overall the topic presented in the paper is very relevant to the workshop. It seems that the authors are describing  a very interesting ongoing work, which could foster both a fruitful discussion during the workshop and several research works  in the near future. Here some questions/comments for the authors:
- The authors present the HADR Aid Delivery Problem with the correlated optimisation criterion (load-balancing across all ports). Is it possible to define a similar optimisation criterion for the privacy requirements?
- Authors talk about strategies for the automated adjustments of the scheduling process to ensure privacy requirements, this fact seems strictly correlated with the concept of "remediation strategy" used in Section "Privacy-aware Workflow Adaptation". In general, the automatic generation of a remediation strategy can be seen as a planning problem?
rating: 4: Top 50% of accepted papers, clear accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct

From: icaps-conference.org/ICAPS/2019/Workshop/SPARK/Paper9/AnonReviewer1
title: Novel and interesting scheduling problem and approach to solve it. Lack systematic evaluation.
review: This paper introduces a very interesting and compelling scheduling problem where privacy is the center theme where information from involved entities need to be hidden and should not be revealed through the final scheduling solution. The authors demonstrated the concept through concrete application of scheduling coalition operations of delivering aid from multiple aid providers to a single aid recipient by ships.

The paper is clearly written with good examples to illustrate different concepts. The scheduling problem setup is novel with lots of challenges. The detailed discussion on the solution approach through MPC and how to use QIF framework to reason about the level of private data that can be inferred, with clear examples, is interesting. Given the novelty of the problem setup and the concrete application scenario, just introducing this type of scheduling problem to the planning and scheduling community to me is a strong enough reason to accept the paper.

The current weak point of the paper is the technical contribution from the planning and scheduling algorithm and evaluation. The actual scheduling algorithm proposed at the moment is a somewhat simple greedy algorithm targeting the main objective function of optimizing for load-balancing (btw, are other popular scheduling objective functions such as makespan etc have been considered by the users?). While there are mentioning here and there in the paper about different concrete problem setup for different tests of different components, there is currently no systematic evaluation on the set of instances of this domain/application. If possible, I would like to see description of the instances that this approach has been implemented on, some analysis of the results to see how the greedy algorithm performed (easy/hard, if backtrackings are required during greedy search, solution quality etc) based on different requirements on QIF; also, the user feedback on the solutions provided by this approach.

rating: 5: Top 15% of accepted papers, strong accept
confidence: 2: The reviewer is fairly confident that the evaluation is correct