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 $\ell$ with elements in $\{1,\ldots,n\}$, and $\le 1/\text{poly}(\ell)$ error, we show that when $\ell = O(2^{\sqrt{\log n}})$, "random seed" adversarially robust algorithms, which only use randomness at initialization, require $\ell^{Ω(1)}$ bits of space, while "random tape" adversarially robust algorithms, which may make random decisions at any time, may use $O(\text{polylog}(\ell))$ space. When $\ell$ is between $n^{Ω(1)}$ and $O(\sqrt{n})$, "random tape" adversarially robust algorithms need $\ell^{Ω(1)}$ space, while "random oracle" adversarially robust algorithms, which can read from a long random string for free, may use $O(\text{polylog}(\ell))$ 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.

The maximum coverage problem is to select $k$ sets from a collection of sets such that the cardinality of the union of the selected sets is maximized. We consider $(1-1/e-ε)$-approximation algorithms for this NP-hard problem in three standard data stream models. 1. {\em Dynamic Model.} The stream consists of a sequence of sets being inserted and deleted. Our multi-pass algorithm uses $ε^{-2} k \cdot \text{polylog}(n,m)$ space. The best previous result (Assadi and Khanna, SODA 2018) used $(n +ε^{-4} k) \text{polylog}(n,m)$ space. While both algorithms use $O(ε^{-1} \log n)$ passes, our analysis shows that when $ε$ is a constant, it is possible to reduce the number of passes by a $1/\log \log n$ factor without incurring additional space. 2. {\em Random Order Model.} In this model, there are no deletions and the sets forming the instance are uniformly randomly permuted to form the input stream. We show that a single pass and $k \text{polylog}(n,m)$ space suffices for arbitrary small constant $ε$. The best previous result, by Warneke et al.~(ESA 2023), used $k^2 \text{polylog}(n,m)$ space. 3. {\em Insert-Only Model.} Lastly, our results, along with numerous previous results, use a sub-sampling technique introduced by McGregor and Vu (ICDT 2017) to sparsify the input instance. We explain how this technique and others used in the paper can be implemented such that the amortized update time of our algorithm is polylogarithmic. This also implies an improvement of the state-of-the-art insert only algorithms in terms of the update time: $\text{polylog}(m,n)$ update time suffices whereas the best previous result by Jaud et al.~(SEA 2023) required update time that was linear in $k$.

Graph coloring is a fundamental problem with wide reaching applications in various areas including ata mining and databases, e.g., in parallel query optimization. In recent years, there has been a growing interest in solving various graph coloring problems in the streaming model. The initial algorithms in this line of work are all crucially randomized, raising natural questions about how important a role randomization plays in streaming graph coloring. A couple of very recent works prove that deterministic or even adversarially robust coloring algorithms (that work on streams whose updates may depend on the algorithm's past outputs) are considerably weaker than standard randomized ones. However, there is still a significant gap between the upper and lower bounds for the number of colors needed (as a function of the maximum degree Δ) for robust coloring and multipass deterministic coloring. We contribute to this line of work by proving the following results.

We consider the problem of space-efficiently estimating the number of simplices in a hypergraph stream. This is the most natural hypergraph generalization of the highly-studied problem of estimating the number of triangles in a graph stream. Our input is a k-uniform hypergraph H with n vertices and m hyperedges, each hyperedge being a k-sized subset of vertices. A k-simplex in H is a subhypergraph on k+1 vertices X such that all k+1 possible hyperedges among X exist in H. The goal is to process the hyperedges of H, which arrive in an arbitrary order as a data stream, and compute a good estimate of T_k(H), the number of k-simplices in H. We design a suite of algorithms for this problem. As with triangle-counting in graphs (which is the special case k = 2), sublinear space is achievable but only under a promise of the form T_k(H) ≥ T. Under such a promise, our algorithms use at most four passes and together imply a space bound of O(ε^{-2} log δ^{-1} polylog n ⋅ min{(m^{1+1/k})/T, m/(T^{2/(k+1)})}) for each fixed k ≥ 3, in order to guarantee an estimate within (1±ε)T_k(H) with probability ≥ 1-δ. We also give a simpler 1-pass algorithm that achieves O(ε^{-2} log δ^{-1} log n⋅ (m/T) (Δ_E + Δ_V^{1-1/k})) space, where Δ_E (respectively, Δ_V) denotes the maximum number of k-simplices that share a hyperedge (respectively, a vertex), which generalizes a previous result for the k = 2 case. We complement these algorithmic results with space lower bounds of the form Ω(ε^{-2}), Ω(m^{1+1/k}/T), Ω(m/T^{1-1/k}) and Ω(mΔ_V^{1/k}/T) for multi-pass algorithms and Ω(mΔ_E/T) for 1-pass algorithms, which show that some of the dependencies on parameters in our upper bounds are nearly tight. Our techniques extend and generalize several different ideas previously developed for triangle counting in graphs, using appropriate innovations to handle the more complicated combinatorics of hypergraphs.

A streaming algorithm is considered to be adversarially robust if it provides correct outputs with high probability even when the stream updates are chosen by an adversary who may observe and react to the past outputs of the algorithm. We grow the burgeoning body of work on such algorithms in a new direction by studying robust algorithms for the problem of maintaining a valid vertex coloring of an $n$-vertex graph given as a stream of edges. Following standard practice, we focus on graphs with maximum degree at most $Δ$ and aim for colorings using a small number $f(Δ)$ of colors. A recent breakthrough (Assadi, Chen, and Khanna; SODA~2019) shows that in the standard, non-robust, streaming setting, $(Δ+1)$-colorings can be obtained while using only $\widetilde{O}(n)$ space. Here, we prove that an adversarially robust algorithm running under a similar space bound must spend almost $Ω(Δ^2)$ colors and that robust $O(Δ)$-coloring requires a linear amount of space, namely $Ω(nΔ)$. We in fact obtain a more general lower bound, trading off the space usage against the number of colors used. From a complexity-theoretic standpoint, these lower bounds provide (i)~the first significant separation between adversarially robust algorithms and ordinary randomized algorithms for a natural problem on insertion-only streams and (ii)~the first significant separation between randomized and deterministic coloring algorithms for graph streams, since deterministic streaming algorithms are automatically robust. We complement our lower bounds with a suite of positive results, giving adversarially robust coloring algorithms using sublinear space. In particular, we can maintain an $O(Δ^2)$-coloring using $\widetilde{O}(n \sqrtΔ)$ space and an $O(Δ^3)$-coloring using $\widetilde{O}(n)$ space.

We study graph computations in an enhanced data streaming setting, where a space-bounded client reading the edge stream of a massive graph may delegate some of its work to a cloud service. We seek algorithms that allow the client to verify a purported proof sent by the cloud service that the work done in the cloud is correct. A line of work starting with Chakrabarti et al. (ICALP 2009) has provided such algorithms, which we call schemes, for several statistical and graph-theoretic problems, many of which exhibit a tradeoff between the length of the proof and the space used by the streaming verifier. This work designs new schemes for a number of basic graph problems---including triangle counting, maximum matching, topological sorting, and single-source shortest paths---where past work had either failed to obtain smooth tradeoffs between these two key complexity measures or only obtained suboptimal tradeoffs. Our key innovation is having the verifier compute certain nonlinear sketches of the input stream, leading to either new or improved tradeoffs. In many cases, our schemes in fact provide optimal tradeoffs up to logarithmic factors. Specifically, for most graph problems that we study, it is known that the product of the verifier's space cost $v$ and the proof length $h$ must be at least $Ω(n^2)$ for $n$-vertex graphs. However, matching upper bounds are only known for a handful of settings of $h$ and $v$ on the curve $h \cdot v=\tildeΘ(n^2)$. For example, for counting triangles and maximum matching, schemes with costs lying on this curve are only known for $(h=\tilde{O}(n^2), v=\tilde{O}(1))$, $(h=\tilde{O}(n), v=\tilde{O}(n))$, and the trivial $(h=\tilde{O}(1), v=\tilde{O}(n^2))$. A major message of this work is that by exploiting nonlinear sketches, a significant ``portion'' of costs on the tradeoff curve $h \cdot v = n^2$ can be achieved.

We study the problem of coloring a given graph using a small number of colors in several well-established models of computation for big data. These include the data streaming model, the general graph query model, the massively parallel communication (MPC) model, and the CONGESTED-CLIQUE and the LOCAL models of distributed computation. On the one hand, we give algorithms with sublinear complexity, for the appropriate notion of complexity in each of these models. Our algorithms color a graph G using κ(G)⋅(1+o(1)) colors, where κ(G) is the degeneracy of G: this parameter is closely related to the arboricity α(G). As a function of κ(G) alone, our results are close to best possible, since the optimal number of colors is κ(G)+1. For several classes of graphs, including real-world "big graphs," our results improve upon the number of colors used by the various (Δ(G)+1)-coloring algorithms known for these models, where Δ(G) is the maximum degree in G, since Δ(G) ⩾ κ(G) and can in fact be arbitrarily larger than κ(G). On the other hand, we establish certain lower bounds indicating that sublinear algorithms probably cannot go much further. In particular, we prove that any randomized coloring algorithm that uses at most κ(G)+O(1) colors would require Ω(n²) storage in the one pass streaming model, and Ω(n²) many queries in the general graph query model, where n is the number of vertices in the graph. These lower bounds hold even when the value of κ(G) is known in advance; at the same time, our upper bounds do not require κ(G) to be given in advance.

We consider directed graph algorithms in a streaming setting, focusing on problems concerning orderings of the vertices. This includes such fundamental problems as topological sorting and acyclicity testing. We also study the related problems of finding a minimum feedback arc set (edges whose removal yields an acyclic graph), and finding a sink vertex. We are interested in both adversarially-ordered and randomly-ordered streams. For arbitrary input graphs with edges ordered adversarially, we show that most of these problems have high space complexity, precluding sublinear-space solutions. Some lower bounds also apply when the stream is randomly ordered: e.g., in our most technical result we show that testing acyclicity in the p-pass random-order model requires roughly n1+1/p space. For other problems, random ordering can make a dramatic difference: e.g., it is possible to find a sink in an acyclic tournament in the onepass random-order model using polylog(n) space whereas under adversarial ordering roughly n1/p space is necessary and sufficient given Θ(p) passes. We also design sublinear algorithms for the feedback arc set problem in tournament graphs; for random graphs; and for randomly ordered streams. In some cases, we give lower bounds establishing that our algorithms are essentially space-optimal. Together, our results complement the much maturer body of work on algorithms for undirected graph streams.

We give new algorithms in the annotated data streaming setting - also known as verifiable data stream computation - for certain graph problems. This setting is meant to model outsourced computation, where a space-bounded verifier limited to sequential data access seeks to overcome its computational limitations by engaging a powerful prover, without needing to trust the prover. As is well established, several problems that admit no sublinear-space algorithms under traditional streaming do allow protocols using a sublinear amount of prover/verifier communication and sublinear-space verification. We give algorithms for many well-studied graph problems including triangle counting, its generalization to subgraph counting, maximum matching, problems about the existence (or not) of short paths, finding the shortest path between two vertices, and testing for an independent set. While some of these problems have been studied before, our results achieve new tradeoffs between space and communication costs that were hitherto unknown. In particular, two of our results disprove explicit conjectures of Thaler (ICALP, 2016) by giving triangle counting and maximum matching algorithms for n-vertex graphs, using o(n) space and o(n^2) communication.

In the setting of streaming interactive proofs (SIPs), a client (verifier) needs to compute a given function on a massive stream of data, arriving online, but is unable to store even a small fraction of the data. It outsources the processing to a third party service (prover) but is unwilling to blindly trust answers returned by this service. Thus, the service cannot simply supply the desired answer; it must convince the verifier of its correctness via a short interaction after the stream has been seen. In this work we study “barely interactive” SIPs. Specifically, we show that one or two rounds of interaction suffice to solve several query problems---including index, median, nearest neighbor search, pattern matching, and range counting---with polylogarithmic space and communication costs. Such efficiency with $O(1)$ rounds of interaction was thought to be impossible based on previous work. On the other hand, we initiate a formal study of the limitations of constant-round SIPs by introducing a new hierarchy of communication models called online interactive proofs (OIPs). The online nature of these models is analogous to the streaming restriction placed upon the verifier in a SIP. We give upper and lower bounds that (1) characterize, up to quadratic blowups, every finite level of the OIP hierarchy in terms of other well-known communication complexity classes, (2) separate the first four levels of the hierarchy, and (3) reveal that the hierarchy collapses to the fourth level. Our study of OIPs reveals marked contrasts and some parallels with the classic Turing machine theory of interactive proofs, establishes limits on the power of existing techniques for developing constant-round SIPs, and provides a new characterization of (nononline) Arthur--Merlin communication in terms of an online model.

We revisit the much-studied problem of space-efficiently estimating the number of triangles in a graph stream, and extensions of this problem to counting fixed-sized cliques and cycles. For the important special case of counting triangles, we give a 4-pass, (1 +/- epsilon)-approximate, randomized algorithm using O-tilde(epsilon^(-2) m^(3/2) / T) space, where m is the number of edges and T is a promised lower bound on the number of triangles. This matches the space bound of a recent algorithm (McGregor et al., PODS 2016), with an arguably simpler and more general technique. We give an improved multi-pass lower bound of Omega(min{m^(3/2)/T , m/sqrt(T)}), applicable at essentially all densities Omega(n) <= m <= O(n^2). We prove other multi-pass lower bounds in terms of various structural parameters of the input graph. Together, our results resolve a couple of open questions raised in recent work (Braverman et al., ICALP 2013). Our presentation emphasizes more general frameworks, for both upper and lower bounds. We give a sampling algorithm for counting arbitrary subgraphs and then improve it via combinatorial means in the special cases of counting odd cliques and odd cycles. Our results show that these problems are considerably easier in the cash-register streaming model than in the turnstile model, where previous work had focused. We use Turán graphs and related gadgets to derive lower bounds for counting cliques and cycles, with triangle-counting lower bounds following as a corollary.

We develop a paradigm for studying multi-player deterministic communication, based on a novel combinatorial concept that we call a strong fooling set. Our paradigm leads to optimal lower bounds on the per-player communication required for solving multi-player EQUALITY problems in a private-message setting. This in turn gives a very strong - O(1) versus Ω(n) - separation between private-message and one-way blackboard communication complexities. Applying our communication complexity results, we show that for deterministic data streaming algorithms, even loose estimations of some basic statistics of an input stream require large amounts of space. For instance, approximating the frequency moment Fkwithin a factor α requires Ω(n/α1/(1-k)) space for k > 1 and roughly Ω(n/αk/(k-1)) space for k > 1. In particular, approximation within any constant factor α, however large, requires linear space, with the trivial exception of k = 1. This is in sharp contrast to the situation for randomized streaming algorithms, which can approximate Fkto within (1±ε) factors using Õ(1) space for k ≤ 2 and o(n) space for all finite k and all constant ε > 0. Previous linear-space lower bounds for deterministic estimation were limited to small factors α, such as α0or F2. We also provide certain space/approximation tradeoffs in a deterministic setting for the problems of estimating the empirical entropy of a stream as well as the size of the maximum matching and the edge connectivity of a streamed graph.

Set cover, over a universe of size n, may be modelled as a data-streaming problem, where the m sets that comprise the instance are to be read one by one. A semi-streaming algorithm is allowed only O(npoly{logn, logm}) space to process this stream. For each p ≥ 1, we give a very simple deterministic algorithm that makes p passes over the input stream and returns an appropriately certified (p + 1)n1/(p+1)-approximation to the optimum set cover. More importantly, we proceed to show that this approximation factor is essentially tight, by showing that a factor better than 0.99n1/(p+1)/(p + 1)2 is unachievable for a p-pass semi-streaming algorithm, even allowing randomisation. In particular, this implies that achieving a Θ(log n)-approximation requires Ω (log n/log log n) passes, which is tight up to the log log n factor. These results extend to a relaxation of the set cover problem where we are allowed to leave an ∊ fraction of the universe uncovered: the tight bounds on the best approximation factor achievable in p passes turn out to be Θp(min{n1/(p+1), ∊–1/p}). Our lower bounds are based on a construction of a family of high-rank incidence geometries, which may be thought of as vast generalisations of affine planes. This construction, based on algebraic techniques, appears flexible enough to find other applications and is therefore interesting in its own right.

In this paper, we study the maximum density, threshold and emptiness queries for intervals in the streaming model. The input is a stream S of n points in the real line R and a floating closed interval W of width alpha. The specific problems we consider in this paper are as follows. - Maximum density: find a placement of W in R containing the maximum number of points of S. - Threshold query: find a placement of W in R, if it exists, that contains at least Delta elements of S. - Emptiness query: find, if possible, a placement of W within the extent of S so that the interior of W does not contain any element of S. The stream S, being huge, does not fit into main memory and can be read sequentially at most a constant number of times, usually once. The problems studied here in the geometric setting have relations to frequency estimation and heavy hitter identification in a stream of data. We provide lower bounds and results on trade-off between extra space and quality of solution. We also discuss generalizations for the higher dimensional variants for a few cases.

The EQUALITY problem is usually one’s first encounter with communication complexity and is one of the most fundamental problems in the field. Although its deterministic and randomized communication complexity were settled decades ago, we find several new things to say about the problem by focusing on three subtle aspects. The first is to consider the expected communication cost (at a worst-case input) for a protocol that uses limited interaction—i.e., a bounded number of rounds of communication—and whose error probability is zero or close to it. The second is to treat the false negative error rate separately from the false positive error rate. The third is to consider the information cost of such protocols. We obtain asymptotically optimal rounds-versus-cost tradeoffs for EQUALITY: both expected communication cost and information cost scale as Theta(log log ... log n), with r-1 logs, where r is the number of rounds. These bounds hold even when the false negative rate approaches 1. For the case of zero-error communication cost, we obtain essentially matching bounds, up to a tiny additive constant. We also provide some applications.

We consider the following fundamental communication problem - there is data that is distributed among servers, and the servers want to compute the intersection of their data sets, e.g., the common records in a relational database. They want to do this with as little communication and as few messages (rounds) as possible. They are willing to use randomization, and fail with a tiny probability. Given a protocol for computing the intersection, it can also be used to compute the exact Jaccard similarity, the rarity, the number of distinct elements, and joins between databases. Computing the intersection is at least as hard as the set disjointness problem, which asks whether the intersection is empty. Formally, in the two-server setting, the players hold subsets S, T ⊆ [n]. In many realistic scenarios, the sizes of S and T are significantly smaller than n, so we impose the constraint that |S|, |T| ≤ k. We study the minimum number of bits the parties need to communicate in order to compute the intersection set S ∩ T, given a certain number r of messages that are allowed to be exchanged. While O(k log (n/k)) bits is achieved trivially and deterministically with a single message, we ask what is possible with more than one message and with randomization. We give a smooth communication/round tradeoff which shows that with O(log* k) rounds, O(k) bits of communication is possible, which improves upon the trivial protocol by an order of magnitude. This is in contrast to other basic problems such as computing the union or symmetric difference, for which Ω(k log(n/k)) bits of communication is required for any number of rounds. For two players, known lower bounds for the easier problem of set disjointness imply our algorithms are optimal up to constant factors in communication and number of rounds. We extend our protocols to $m$-player protocols, obtaining an optimal O(mk) bits of communication with a similarly small number of rounds.

Motivated by the surging popularity of commercial cloud computing services, a number of recent works have studied annotated data streams and variants thereof. In this setting, a computationally weak verifier (cloud user), lacking the resources to store and manipulate his massive input locally, accesses a powerful but untrusted prover (cloud service). The verifier must work within the restrictive data streaming paradigm. The prover, who can annotate the data stream as it is read, must not just supply the final answer but also convince the verifier of its correctness. Ideally, both the amount of annotation from the prover and the space used by the verifier should be sublinear in the relevant input size parameters. A rich theory of such algorithms—which we call schemes—has started to emerge. Prior work has shown how to leverage the prover's power to efficiently solve problems that have no non-trivial standard data stream algorithms. However, even though optimal schemes are now known for several basic problems, such optimality holds only for streams whose length is commensurate with the size of the data universe. In contrast, many real-world data sets are relatively sparse, including graphs that contain only o(n2) edges, and IP traffic streams that contain much fewer than the total number of possible IP addresses, 2128 in IPv6. Here we design the first annotation schemes that allow both the annotation and the space usage to be sublinear in the total number of stream updates rather than the size of the data universe. We solve significant problems, including variations of INDEX, SET-DISJOINTNESS, and FREQUENCY-MOMENTS, plus several natural problems on graphs. On the other hand, we give a new lower bound that, for the first time, rules out smooth tradeoffs between annotation and space usage for a specific problem. Our technique brings out new nuances in Merlin-Arthur communication complexity models, and provides a separation between online versions of the MA and AMA models.

The central goal of data stream algorithms is to process massive streams of data using sublinear storage space. Motivated by work in the database community on outsourcing database and data stream processing, we ask whether the space usage of such algorithms can be further reduced by enlisting a more powerful “helper” that can annotate the stream as it is read. We do not wish to blindly trust the helper, so we require that the algorithm be convinced of having computed a correct answer. We show upper bounds that achieve a nontrivial tradeoff between the amount of annotation used and the space required to verify it. We also prove lower bounds on such tradeoffs, often nearly matching the upper bounds, via notions related to Merlin-Arthur communication complexity. Our results cover the classic data stream problems of selection, frequency moments, and fundamental graph problems such as triangle-freeness and connectivity. Our work is also part of a growing trend—including recent studies of multipass streaming, read/write streams, and randomly ordered streams—of asking more complexity-theoretic questions about data stream processing. It is a recognition that, in addition to practical relevance, the data stream model raises many interesting theoretical questions in its own right.

Sampling-based probabilistic roadmap algorithms such as PRM and PRM* have been shown to be effective at solving certain motion planning problems, but the large graphs generated to express the connectivity and a metric on the configuration space may require much storage space and be expensive to search. Recent work by Marble and Bekris [14], [19] applied spanner algorithms to PRM* these algorithms prune some edges in a dense graph, while guaranteeably maintaining an approximation to the metric. In this paper, we apply (and improve) a state-of-the-art streaming spanner algorithm to prune PRM* roadmaps. The algorithm we present has the main advantage of computational speed; when applied to PRM*, the processing time per vertex is independent of the number of sampled vertices, n, as compared to O(nlog2nloglogn) in [19]. In practice, the algorithm we present prunes a graph with about 20 million edges in less than 20 seconds on a modern desktop computer; compared to the time required for generating such a roadmap, this additional processing time is essentially trivial. In fact, because the combination of this algorithm with PRM* avoids the need for many collision detections, the combination runs several times faster than PRM*alone.

This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of augmented-index. In contrast to analogous results for index by Jain, Radhakrishnan, and Sen [J. ACM, 56 (2009), article 33], we have to overcome the significant technical challenge that protocols for augmented-index may violate the “rectangle property” due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu, and Nayak [Proceedings of the 42 nd Annual ACM Symposium on Theory of Computing, 2010, pp. 261--270] that asked about the multipass complexity of recognizing Dyck languages. This results in a natural separation between the standard multipass model and the multipass model that permits reverse passes. Third, we present the first passive memory checkers that verify the interaction transcripts of priority queues, stacks, and double-ended queues. We obtain tight upper and lower bounds for these problems, thereby addressing an important subclass of the memory checking framework of Blum et al. [Algorithmica, 12 (1994), pp. 225--244].

Let G=(V,E) be a supply graph and H=(V,F) a demand graph defined on the same set of vertices. An assignment of capacities to the edges of G and demands to the edges of H is said to satisfy the cut condition if for any cut in the graph, the total demand crossing the cut is no more than the total capacity crossing it. The pair (G,H) is called cut-sufficient if for any assignment of capacities and demands that satisfy the cut condition, there is a multiflow routing the demands defined on $H$ within the network with capacities defined on G.

We prove an optimal $\Omega(n)$ lower bound on the randomized communication complexity of the much-studied gap-hamming-distance problem. As a consequence, we obtain essentially optimal multipass space lower bounds in the data stream model for a number of fundamental problems, including the estimation of frequency moments. The gap-hamming-distance problem is a communication problem, wherein Alice and Bob receive $n$-bit strings $x$ and $y$, respectively. They are promised that the Hamming distance between $x$ and $y$ is either at least $n/2+\sqrt{n}$ or at most $n/2-\sqrt{n}$, and their goal is to decide which of these is the case. Since the formal presentation of the problem by Indyk and Woodruff [Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science, 2003, pp. 283--289], it had been conjectured that the naïve protocol, which uses $n$ bits of communication, is asymptotically optimal. The conjecture was shown to be true in several special cases, e.g., when the communication is deterministic or when the number of rounds of communication is limited. The proof of our aforementioned result, which settles this conjecture fully, is based on a new geometric statement regarding correlations in Gaussian space, related to a result of Borell [Z. Wahrsch. Verw. Gebiete, 70 (1985), pp. 1--13]. To prove this geometric statement, we show that random projections of not-too-small sets in Gaussian space are close to a mixture of translated normal variables.

We prove an optimal Ω(n) lower bound on the randomized communication complexity of the much-studied Gap-Hamming-Distance problem. As a consequence, we obtain essentially optimal multi-pass space lower bounds in the data stream model for a number of fundamental problems, including the estimation of frequency moments.

We study the problem of finding a most profitable subset of n given tasks, each with a given start and finish time as well as profit and resource requirement, that at no time exceeds the quantity B of available resource. We show that this NP-hard Resource Allocation problem can be (1/2 − ε)-approximated in randomized polynomial time, which improves upon earlier approximation results.

This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of AUGMENTED-INDEX. In contrast to analogous results for INDEX by Jain, Radhakrishnan and Sen [J. ACM, 2009], we have to overcome the significant technical challenge that protocols for AUGMENTED-INDEX may violate the "rectangle property" due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu and Nayak [STOC, 2010] on the multi-pass complexity of recognizing Dyck languages. This results in a natural separation between the standard multi-pass model and the multi-pass model that permits reverse passes. Third, we present the first passive memory checkers that verify the interaction transcripts of priority queues, stacks, and double-ended queues. We obtain tight upper and lower bounds for these problems, thereby addressing an important sub-class of the memory checking framework of Blum et al. [Algorithmica, 1994].

We consider the approximate nearest neighbor search problem on the Hamming cube $\{0,1\}^d$. We show that a randomized cell probe algorithm that uses polynomial storage and word size $d^{O(1)}$ requires a worst case query time of $\Omega({\rm log}\,{\rm log}\,d/{\rm log}\,{\rm log}\,{\rm log}\,d)$. The approximation factor may be as loose as $2^{{\rm log}^{1-\eta}d}$ for any fixed $\eta>0$. Our result fills a major gap in the study of this problem since all earlier lower bounds either did not allow randomization [A. Chakrabarti et al., A lower bound on the complexity of approximate nearest-neighbor searching on the Hamming cube, in Discrete and Computational Geometry, Springer, Berlin, 2003, pp. 313–328; D. Liu, Inform. Process. Lett., 92 (2004), pp. 23–29] or did not allow approximation [A. Borodin, R. Ostrovsky, and Y. Rabani, Proceedings of the 31st Annual ACM Symposium on Theory of Computing, 1999, pp. 312–321; O. Barkol and Y. Rabani, Proceedings of the 32nd Annual ACM Symposium on Theory of Computing, 2000, pp. 388–396; T. S. Jayram et al., J. Comput. System Sci., 69 (2004), pp. 435–447]. We also give a cell probe algorithm that proves that our lower bound is optimal. Our proof uses a lower bound on the round complexity of the related communication problem. We show, additionally, that considerations of bit complexity alone cannot prove any nontrivial cell probe lower bound for the problem. This shows that the “richness technique” [P. B. Miltersen et al., J. Comput. System Sci., 57 (1998), pp. 37–49] used in a lot of recent research around this problem would not have helped here. Our proof is based on information theoretic techniques for communication complexity, a theme that has been prominent in recent research [A. Chakrabarti et al., Proceedings of the 42nd Annual IEEE Symposium on Foundations of Computer Science, 2001, pp. 270–278; Z. Bar-Yossef et al., Proceedings of the 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002, pp. 209–218; P. Sen, Proceedings of the 18th Annual IEEE Conference on Computational Complexity, 2003, pp. 73–83; R. Jain, J. Radhakrishnan, and P. Sen, Proceedings of the 30th International Colloquium on Automata, Languages and Programming, 2003, pp. 300–315].

We describe a simple algorithm for approximating the empirical entropy of a stream of m values up to a multiplicative factor of (1+ϵ) using a single pass, O (ϵ −2 log (δ −1 ) log m ) words of space, and O (log ϵ −1 + log log δ −1 + log log m ) processing time per item in the stream. Our algorithm is based upon a novel extension of a method introduced by Alon et al. [1999]. This improves over previous work on this problem. We show a space lower bound of Ω(ϵ −2 /log 2 (ϵ −1 )), demonstrating that our algorithm is near-optimal in terms of its dependency on ϵ. We show that generalizing to multiplicative-approximation of the k th-order entropy requires close to linear space for k ≥1. In contrast we show that additive-approximation is possible in a single pass using only poly-logarithmic space. Lastly, we show how to compute a multiplicative approximation to the entropy of a random walk on an undirected graph.

The gap-Hamming-distance problem arose in the context of proving space lower bounds for a number of key problems in the data stream model. In this problem, Alice and Bob have to decide whether the Hamming distance between their n-bit input strings is large (i.e., at least n/2 + radic(n)) or small (i.e., at most n/2 - radic(n)); they do not care if it is neither large nor small. This Theta(radic(n)) gap in the problem specification is crucial for capturing the approximation allowed to a data stream algorithm. Thus far, for randomized communication, an Omega(n) lower bound on this problem was known only in the one-way setting. We prove an Omega(n) lower bound for randomized protocols that use any constant number of rounds. As a consequence we conclude, for instance, that epsiv-approximately counting the number of distinct elements in a data stream requires Omega(1/epsiv2) space, even with multiple (a constant number of) passes over the input stream. This extends earlier one-pass lower bounds, answering a long-standing open question. We obtain similar results for approximating the frequency moments and for approximating the empirical entropy of a data stream. In the process, we also obtain tight n - Theta(radic(n)log n) lower and upper bounds on the one-way deterministic communication complexity of the problem. Finally, we give a simple combinatorial proof of an Omega(n) lower bound on the one-way randomized communication complexity.

We study the properties of embeddings, multicommodity flows, and sparse cuts in minor-closed families of graphs which are also closed under 2-sums; this includes planar graphs, graphs of bounded treewidth, and constructions based on recursive edge replacement.

We study the one-way number-on-the-forehead (NOF) communication complexity of the $k$-layer pointer jumping problem with $n$ vertices per layer. This classic problem, which has connections to many aspects of complexity theory, has seen a recent burst of research activity, seemingly preparing the ground for an $Omega(n)$ lower bound, for constant $k$. Our first result is a surprising sublinear --- i.e., $o(n)$ --- upper bound for the problem that holds for $k ge 3$, dashing hopes for such a lower bound. A closer look at the protocol achieving the upper bound shows that all but one of the players involved are collapsing, i.e., their messages depend only on the composition of the layers ahead of them. We consider protocols for the pointer jumping problem where all players are collapsing. Our second result shows that a strong $n - O(log n)$ lower bound does hold in this case. Our third result is another upper bound showing that nontrivial protocols for (a non-Boolean version of) pointer jumping are possible even when all players are collapsing. Our lower bound result uses a novel proof technique, different from those of earlier lower bounds that had an information-theoretic flavor. We hope this is useful in further study of the problem.

We study the communication complexity of evaluating functions when the input data is randomly allocated (according to some known distribution) amongst two or more players, possibly with information overlap. This naturally extends previously studied variable partition models such as the best-case and worst-case partition models [32,29]. We aim to understand whether the hardness of a communication problem holds for almost every allocation of the input, as opposed to holding for perhaps just a few atypical partitions.

We consider the k-layer pointer jumping problem in the one-way multi-party number-on-the-forehead communication model. Sufficiently strong lower bounds for the problem would have major consequences in circuit complexity. We take an information complexity approach to this problem and obtain three lower bounds that improve upon earlier work. For myopic protocols (where players may see only one layer ahead but arbitrarily far behind), we greatly improve a lower bound due to Gronemeier (2006). Our new lower bound is Omega(n/k), where n is the number of vertices per layer. For conservative protocols (where players may see arbitrarily far ahead but not behind, instead seeing only the vertex reached by following the pointers up to their layer), we extend an Omega(n/k2) lower bound due to Damm, Jukna and Sgall (1998) so that it applies for all k. The above two bounds apply even to the Boolean version of pointer jumping. Our third lower bound is for the non-Boolean case and for k les log* n. We obtain an Omega(n log(k-1)n) bound for myopic protocols. Damm et al. had obtained a similar bound for deterministic conservative protocols. All our lower bounds apply directly to randomised protocols.

Recent research has identified significant vulnerabilities in recommender systems. Shilling attacks, in which attackers introduce biased ratings in order to influence future recommendations, have been shown to be effective against collaborative filtering algorithms. We postulate that the distribution of item ratings in time can reveal the presence of a wide range of shilling attacks given reasonable assumptions about their duration. To construct a time series of ratings for an item, we use a window size of k to group consecutive ratings for the item into disjoint windows and compute the sample average and sample entropy in each window. We derive a theoretically optimal window size to best detect an attack event if the number of attack profiles is known. For practical applications where this number is unknown, we propose a heuristic algorithm that adaptively changes the window size. Our experimental results demonstrate that monitoring rating distributions in time series is an effective approach for detecting shilling attacks.

We study the Unsplittable Flow Problem (UFP) on line graphs and cycles, focusing on the long-standing open question of whether the problem is APX-hard. We describe a deterministic quasi-polynomial time approximation scheme for UFP on line graphs, thereby ruling out an APX-hardness result, unless NP ⊆ DTIME(2polylog(n)). Our result requires a quasi-polynomial bound on all edge capacities and demands in the input instance. We extend this result to undirected cycle graphs.Earlier results on this problem included a polynomial time (2+ε)-approximation under the assumption that no demand exceeds any edge capacity (the "no-bottleneck assumption") and a super-constant integrality gap if this assumption did not hold. Unlike most earlier work on UFP, our results do not require a no-bottleneck assumption.

We consider the approximate nearest neighbour search problem on the Hamming cube {0, 1 }/sup d/. We show that a randomised cell probe algorithm that uses polynomial storage and word size d/sup O(1)/ requires a worst case query time of /spl Omega/ (log log d/ log log log d). The approximation factor may be as loose as 2/sup log 1 - /spl eta//d for any fixed /spl eta/ > 0. This generalises an earlier result (Chakrabarti et al., 1999) on the deterministic complexity of the same problem and, more importantly, fills a major gap in the study of this problem since all earlier lower bounds either did not allow randomisation according to Chakrabarti et al. (1999) and Liu (2003) or did not allow approximation according to Borodin et al. (1999), Barkol and Rabani (2000), and Jayram et al. (2003). We also give a cell probe algorithm which proves that our lower bound is optimal. Our proof uses a lower bound on the round complexity of the related communication problem. We show, additionally, that considerations of bit complexity alone cannot prove any nontrivial cell probe lower bound for the problem. This shows that the richness technique (Miltersen et al., 1995) used in a lot of research around this problem would not have helped here. Our proof is based on information theoretic techniques for communication complexity, a theme that has been prominent in research by Chakrabarti et al. (2001), Bar-Yossef et al. (2002), Sen (2003) and Jain et al. (2003). In particular, we make heavy use of the round elimination and message compression ideas in the work of Sen (2003) and Jain et al. (2003), and also introduce a technique which we call message switching.

Representation of relative spatial relations between objects is often required in many multimedia database applications because spatial relations between objects in an image convey important information about the image. Quantitative representation of spatial relations taking into account shape, size, orientation and distance is often required. The R-Histogram is such a quantitative representation of spatial relations between two objects. However, this method only considers pixels on the object boundary, assuming that the objects are homeomorphic to a 2-ball. For objects with more complicated topology, we propose in this paper the R*-Histogram, a new extension to the R-Histogram. The R*-Histogram generalizes the R-Histogram by taking into account all the pixels in the objects. We also introduce an efficient O(kN log N) time algorithm to compute the R*-Histogram, which is asymptotically faster than the original O(N2) time algorithm for the R-Histogram even when k=O(n). Here, N=n2 denotes the number of pixels in the processed n x n image and k is the number of different directions considered. The effectiveness of the R*-Histogram is evaluated empirically with a Query By Example (QBE) system on a database of 2000 synthetic images containing objects with complicated shape and topology. Experiments have shown that the similarly search results match human intuition very well.

We study the communication complexity of the set disjointness problem in the general multiparty model. For t players, each holding a subset of a universe of size n, we establish a near-optimal lower bound of /spl Omega/(n/(t log t)) on the communication complexity of the problem of determining whether their sets are disjoint. In the more restrictive one-way communication model, in which the players are required to speak in a predetermined order, we improve our bound to an optimal /spl Omega/(n/t). These results improve upon the earlier bounds of /spl Omega/(n/t/sup 2/) in the general model, and /spl Omega/((/spl epsiv//sup 2/n)/t/sup 1+/spl epsiv//) in the one-way model, due to Bar-Yossef, Jayram, Kumar, and Sivakumar (2002). As in the case of earlier results, our bounds apply to the unique intersection promise problem. This communication problem is known to have connections with the space complexity of approximating frequency moments in the data stream model. Our results lead to an improved space complexity lower bound of /spl Omega/(n/sup 1-2/k//log n) for approximating the k/sup th/ frequency moment with a constant number of passes over the input, and a technical improvement to /spl Omega/(n/sup 1-2/k/) if only one pass over the input is permitted. Our proofs rely on the information theoretic direct sum decomposition paradigm of Bar-Yossef et al. [2002]. Our improvements stem from novel analytical techniques, as opposed to earlier techniques based on Hellinger and related distances, for estimating the information cost of protocols for one-bit functions.

Given m copies of the same problem, does it take m times the amount of resources to solve these m problems? This is the direct sum problem, a fundamental question that has been studied in many computational models. We study this question in the simultaneous message (SM) model of communication introduced by A.C. Yao (1979). The equality problem for n-bit strings is well known to have SM complexity /spl Theta/(/spl radic/n). We prove that solving m copies of the problem has complexity /spl Omega/(m/spl radic/n); the best lower bound provable using previously known techniques is /spl Omega/(/spl radic/(mn)). We also prove similar lower bounds on certain Boolean combinations of multiple copies of the equality function. These results can be generalized to a broader class of functions. We introduce a new notion of informational complexity which is related to SM complexity and has nice direct sum properties. This notion is used as a tool to prove the above results; it appears to be quite powerful and may be of independent interest.

We prove new results on evasiveness of monotone graph properties by extending the techniques of Kahn, Saks, and Sturtevant [Combinatorica, 4 (1984), pp. 297--306]. For the property of containing a subgraph isomorphic to a fixed graph, and a fairly large class of related n-vertex graph properties, we show evasiveness for an arithmetic progression of values of n. This implies a $\frac12n^2 - O(n)$ lower bound on the decision tree complexity of these properties. We prove that properties that are preserved under taking graph minors are evasive for all sufficiently large n. This greatly generalizes a theorem due to Best, van Emde Boas, and Lenstra [A Sharpened Version of the Aanderaa--Rosenberg Conjecture, Report ZW 30/74, Mathematisch Centrum, Amsterdam, The Netherlands, 1974] which states that planarity is evasive. We prove a similar result for bipartite subgraph containment.

We consider the nearest-neighbor problem over the d-cube: given a collection of points in {0, 1} d, find the one nearest to a query point (in the L 1 sense). We establish a lower bound of Ω(log log d/log log log d)ontheworst-casequery time. This result holds in the cell probe model with (any amount of) polynomial storage and word-size d O(1). The same lower bound holds for the approximate version of the problem, where the answer may be any point further than the nearest neighbor by a factor as large as 2 ⌊(log d)1−ε ⌋ , for any fixed ε>0. 1