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Virual Oral Question and Answer
IEEE/ICACT20260329 Question.1
Questioner: xueyy@dsp.ac.cn    2026-02-04 오후 1:39:50 		IEEE/ICACT20260329 Answer.1
Answer by Auhor yangyn@dsp.ac.cn   2026-02-04 오후 1:39:50
In this paper, privacy is preserved by exchanging compressed state summaries (SS) between data centers instead of full resource and topology information. Could this compression lead to the loss of critical information that may affect the global optimality of the generated parallel strategy? Have the authors analyzed the trade-off between privacy strength and optimization performance? The compression of state summaries (SS) is carefully designed to conceal detailed resource and topology information while retaining the minimal maximum stage execution time necessary for global optimization. Our theoretical analysis in Section IV.B.3 proves that CDynP achieves optimality equivalent to the exhaustive enumeration method under privacy constraints, as it leverages the optimal substructure of dynamic programming and ensures no critical information loss through chain-based interactions. Specifically, the SS compression captures the cumulative state from upstream data centers, allowing each DC to make locally optimal decisions without exposing internal data. We have explicitly analyzed the trade-off between privacy strength and optimization efficiency. The pre-defined overlapping areas in the heuristic pre-partitioning phase limit the search space to balance computational cost and solution quality, ensuring that privacy preservation does not compromise global optimality in practice. Experimental results in Section V validate that CDynP maintains leading performance across various scenarios, underscoring its robustness. Thus, the compression mechanism is both effective and privacy-compliant.

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