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Sublinear-Time Computation in the Presence of an Online Adversary

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Sublinear-Time Computation in the Presence of an Online Adversary Sublinear-Time Computation in the Presence of An Online Adversary Iden Kalemaj∗ Sofya Raskhodnikovay Nithin Varmaz June 30, 2021 Abstract We initiate the study of sublinear-time algorithms that access their input via an online adversarial oracle. After answering each query to the input object, such an oracle can erase or corrupt a small number of input values. Our goal is to understand the complexity of basic computational tasks in extremely adversarial situations, where the algorithm's access to data is blocked|or fraudulent values are introduced|during the execution of the algorithm in response to its actions. Specifically, we focus on property testing in the presence of an online oracle that makes t erasures adversarially after answering each query of the algorithm. We show that two fundamental properties of functions, linearity and quadraticity, can be tested for constant t with asymptotically the same complexity as in the standard property testing model. In contrast, some other properties, including sortedness and the Lipschitz property of sequences, cannot be tested at all, even for t = 1: Our investigation leads to a deeper understanding of the structure of violations to these widely studied properties. ∗Department of Computer Science, Boston University. Email: [email protected]. This work was supported by NSF award CCF-1909612 and Boston University's Dean's Fellowship. yDepartment of Computer Science, Boston University. Email: [email protected]. This work was supported by NSF award CCF-1909612. zDepartment of Computer Science, University of Haifa. Email: [email protected]. This work was supported by the Israel Science Foundation, grant number 497/17 and the PBC Fellowship for Postdoctoral Fellows by the Israeli Council of Higher Education. 1 Introduction We initiate the study of sublinear-time algorithms that compute in the presence of an online adversary. After the algorithm gets the answer to each query to the input object, such an adversary can hide or corrupt a small number of input values. It might be helpful to think of the algorithm as trying to detect some fraud and of the adversary as trying to obstruct access to data by hiding or corrupting it in order to make it hard to uncover any evidence. Our goal is to understand the complexity of basic computational tasks in extremely adversarial situations, where the algorithm's access to data is blocked|or fraudulent values are introduced|during the execution of the algorithm in response to its actions. Specifically, we represent the input object as a function f on an arbitrary finite domain1, which the algorithm can access by querying a point x from the domain and receiving the answer O(x) from an oracle. At the beginning of computation, O(x) = f(x) for all points x in the domain of the function. We parameterize our model by a natural number t that controls the number of function values the adversary can modify after the oracle answers each query. Mathematically, we represent the oracle and the adversary as one entity. However, it might be helpful to think of the oracle as the data holder and of the adversary as the obstructionist. A t-online-erasure oracle can replace values O(x) on up to t points x with a special symbol ?, thus erasing them, whereas a t-online-corruption oracle can replace them with arbitrary values in the range. The new values will be used by the oracle to answer future queries to the corresponding points. The locations of erasures and corruptions are unknown to the algorithm. The actions of the oracle can depend on the input, the queries made so far, and even on the publicly known code that the algorithm is running, but not on future coin tosses of the algorithm. We focus on investigating property testing in the presence of online erasures. Some of our results also apply to online corruptions and computational tasks related to property testing, as discussed later. In the property testing model, introduced by [RS96, GGR98] with the goal of formally studying sublinear-time algorithms, a property is represented by a set P (of functions satisfying the desired property). A function f is "-far from P if f differs from each function g 2 P on at least an " fraction of domain points. The goal is to distinguish, with constant probability, functions f 2 P from functions that are "-far from P: We call an algorithm a t-online-erasure-resilient "-tester for property P if, given parameters t 2 N and " 2 (0; 1); and access to an input function f via a t-online-erasure oracle, the algorithm accepts with probability at least 2/3 if f 2 P and rejects with probability at least 2/3 if f is "-far from P. We study the query complexity of online-erasure-resilient testing of several fundamental properties. We show that for linearity and quadraticity of functions f : f0; 1gd ! f0; 1g, the query complexity of t-online- erasure-resilient testing for constant t is asymptotically the same as in the standard model. A function f(x) is linear if it can be represented as a sum of monomials of the form xi, where x = (x1; : : : ; xd) is a vector of d bits; the function is quadratic if it can be represented as a sum of monomials, each of the form xi or xixj. To appreciate the difficulty of testing in the presence of online erasures, consider the case of linearity and t = 1: The celebrated tester for linearity in the standard property testing model was proposed by Blum, Luby, and Rubinfeld [BLR93]. It looks for witnesses of non-linearity that consist of three points x; y; and x ⊕ y satisfying f(x) + f(y) 6= f(x ⊕ y), where addition is mod 2, and ⊕ denotes the bitwise XOR operator. Bellare et al. [BCH+96] show that if f is "-far from linear, then a triple (x; y; x ⊕ y) is a witness with probability at least " when x; y 2 f0; 1gd are chosen uniformly and independently at random. In our model, after x and y are queried, the oracle can erase the value of x⊕y: To address this issue, the tester could query three points x; y, and z, and then uniformly select either x ⊕ z or y ⊕ z. If t = 1, the oracle does not have the erasure budget to erase both x ⊕ z and y ⊕ z, and the tester succeeds in catching a triple of the right form with probability 1/2. However, both triples that the tester could have queried|namely, (x; z; x ⊕ z) and (y; z; y ⊕ z)|have to be witnesses of non-linearity for the tester to succeed in catching a witness. So, we need to understand not only the fraction of triples that are witnesses, but also how they interact. Witnesses of non-quadraticity are even more complicated. The tester of Alon et al. [AKK+05] looks for witnesses consisting of points x; y; z, and all four of their linear combinations. We describe a two-player game that models the interaction between the tester and the adversary and give a winning strategy for the tester- 1Input objects such as strings, sequences, images, matrices, and graphs can all be represented as functions. 1 player. We also consider witness structures in which all specified tuples are witnesses of non-quadraticity. We analyze the probability of getting a witness structure under uniform sampling when the input function is "-far from quadratic. Our investigation leads to a deeper understanding of the structure of witnesses for both properties, linearity and quadraticity. In contrast to linearity and quadraticity, we show that several other properties, specifically, sortedness and the Lipschitz property of sequences, and the Lipschitz property of functions f : f0; 1gd ! f0; 1; 2g cannot be tested in the presence of an online-erasure oracle, even with t = 1, no matter how many queries the algorithm makes. Interestingly, witnesses for these properties have a much simpler structure than witnesses for linearity and quadraticity. Consider the case of sortedness of integer sequences, represented by functions f :[n] ! N: We say that a sequence is sorted (or the corresponding function is monotone) if f(x) ≤ f(y) for all x < y in [n]. A witness of non-sortedness consists of two points x < y, such that f(x) > f(y): In the standard model, sortedness can be "-tested with an algorithm that queries an O(pn=") + log "n uniform and independent points [FLN 02]. (The fastest testers for this property have O( " ) query complexity [EKK+00, DGL+99, BGJ+12, CS13, Bel18], but they make correlated queries that follow a more complicated distribution.) Our impossibility result demonstrates that even the simplest testing strategy of querying independent points can be thwarted by an online adversary. To prove this result, we use sequences that are far from being sorted, but where each point is involved in only one witness, allowing the oracle to erase the second point of the witness as soon as the first one is queried. Using a version of Yao's principle that is suitable for our model, we turn these examples into a general impossibility result for testing sortedness with a 1-online-erasure oracle. Our impossibility result for testing sortedness uses sequences with many (specifically, n) distinct integers. We show that this is not a coincidence by designing a t-online-erasure-resilient sortedness tester that works "2n for sequences that have O( t ) distinct values. However, the number of distinct values does not have to be large to preclude testing the Lipschitz property in our model. A function f :[n] ! N, representing an n-integer sequence, is Lipschitz if jf(x)−f(y)j ≤ jx−yj for all x; y 2 [n].
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