eng
Schloss Dagstuhl β Leibniz-Zentrum fΓΌr Informatik
Leibniz International Proceedings in Informatics
1868-8969
2024-07-15
28:1
28:20
10.4230/LIPIcs.CCC.2024.28
article
Finding Missing Items Requires Strong Forms of Randomness
Chakrabarti, Amit
1
https://orcid.org/0000-0003-3633-9180
Stoeckl, Manuel
1
https://orcid.org/0000-0001-8189-0516
Department of Computer Science, Dartmouth College, Hanover, NH, USA
Adversarially robust streaming algorithms are required to process a stream of elements and produce correct outputs, even when each stream element can be chosen as a function of earlier algorithm outputs. As with classic streaming algorithms, which must only be correct for the worst-case fixed stream, adversarially robust algorithms with access to randomness can use significantly less space than deterministic algorithms. We prove that for the Missing Item Finding problem in streaming, the space complexity also significantly depends on how adversarially robust algorithms are permitted to use randomness. (In contrast, the space complexity of classic streaming algorithms does not depend as strongly on the way randomness is used.)
For Missing Item Finding on streams of length π with elements in {1,β¦,n}, and β€ 1/poly(π) error, we show that when π = O(2^β{log n}), "random seed" adversarially robust algorithms, which only use randomness at initialization, require π^Ξ©(1) bits of space, while "random tape" adversarially robust algorithms, which may make random decisions at any time, may use O(polylog(π)) random bits. When π is between n^Ξ©(1) and O(βn), "random tape" adversarially robust algorithms need π^Ξ©(1) space, while "random oracle" adversarially robust algorithms, which can read from a long random string for free, may use O(polylog(π)) space. The space lower bound for the "random seed" case follows, by a reduction given in prior work, from a lower bound for pseudo-deterministic streaming algorithms given in this paper.
https://drops.dagstuhl.de/storage/00lipics/lipics-vol300-ccc2024/LIPIcs.CCC.2024.28/LIPIcs.CCC.2024.28.pdf
Data streaming
lower bounds
space complexity
adversarial robustness
derandomization
sketching
sampling