The following papers have been accepted for publication in 2019:

Independent sets in vertex-arrival streams (G. Cormode, J. Dark, and C. Konrad). In International Colloquium on Automata, Languages and Programming (ICALP), 2019.

We consider the maximal and maximum independent set problems in three models of graph streams:
In the edge model we see a stream of edges which collectively define a graph; this model is well-studied for a variety of problems. We show that the space complexity for a one-pass streaming algorithm to find a maximal independent set is quadratic (i.e. we must store all edges). We further show that it is not much easier if we only require approximate maximality. This contrasts strongly with the other two vertex-based models, where one can greedily find an exact solution in only the space needed to store the independent set.
In the “explicit” vertex model, the input stream is a sequence of vertices making up the graph. Every vertex arrives along with its incident edges that connect to previously arrived vertices. Various graph problems require substantially less space to solve in this setting than in edge-arrival streams. We show that every one-pass c-approximation streaming algorithm for maximum independent set (MIS) on explicit vertex streams requires Ω((n²)/(c⁶)) bits of space, where n is the number of vertices of the input graph. It is already known that Θ~((n²)/(c²)) bits of space are necessary and sufficient in the edge arrival model (Halldórsson et al. 2012), thus the MIS problem is not significantly easier to solve under the explicit vertex arrival order assumption. Our result is proved via a reduction from a new multi-party communication problem closely related to pointer jumping.
In the “implicit” vertex model, the input stream consists of a sequence of objects, one per vertex. The algorithm is equipped with a function that maps pairs of objects to the presence or absence of edges, thus defining the graph. This model captures, for example, geometric intersection graphs such as unit disc graphs. Our final set of results consists of several improved upper and lower bounds for interval and square intersection graphs, in both explicit and implicit streams. In particular, we show a gap between the hardness of the explicit and implicit vertex models for interval graphs.

Answering range queries under local differential privacy (G. Cormode, T. Kulkarni, and D. Srivastava). In International Conference on Very Large Data Bases (VLDB), 2019

Counting the fraction of a population having an input within a specified interval i.e. a range query, is a fundamental data analysis primitive. Range queries can also be used to compute other core statistics such as quantiles, and to build prediction models. However, frequently the data is subject to privacy concerns when it is drawn from individuals, and relates for example to their financial, health, religious or political status. In this paper, we introduce and analyze methods to support range queries under the local variant of differential privacy, an emerging standard for privacy-preserving data analysis.

The local model requires that each user releases a noisy view of her private data under a privacy guarantee. While many works address the problem of range queries in the trusted aggregator setting, this problem has not been addressed specifically under untrusted aggregation (local DP) model even though many primitives have been developed recently for estimating a discrete distribution. We describe and analyze two classes of approaches for range queries, based on hierarchical histograms and the Haar wavelet transform. We show that both have strong theoretical accuracy guarantees on variance. In practice, both methods are fast and require minimal computation and communication resources. Our experiments show that the wavelet approach is most accurate in high privacy settings, while the hierarchical approach dominates for weaker privacy requirements.

Streaming algorithms for bin packing and vector scheduling (G. Cormode and P. Veselý). In Workshop on Approximation and Online Algorithms, 2019.

Problems involving the efficient arrangement of simple objects, as captured by bin packing and makespan scheduling, are fundamental tasks in combinatorial optimization. These are well understood in the traditional online and offline cases, but have been less well-studied when the volume of the input is truly massive, and cannot even be read into memory. This is captured by the streaming model of computation, where the aim is to approximate the cost of the solution in one pass over the data, using small space. As a result, streaming algorithms produce concise input summaries that approximately preserve the optimum value.

We design the first efficient streaming algorithms for these fundamental problems in combinatorial optimization. For Bin Packing, we provide a streaming asymptotic 1+-approximation with O~(1/ε) memory, where O~ hides logarithmic factors. Moreover, such a space bound is essentially optimal. Our algorithm implies a streaming d+ε-approximation for Vector Bin Packing in d dimensions, running in space O~((d)/(ε)). For the related Vector Scheduling problem, we show how to construct an input summary in space O~(d²·m / ε²) that preserves the optimum value up to a factor of 2 – (1)/(m) +ε, where m is the number of identical machines.

 Efficient interactive proofs for linear algebra (G. Cormode and C. Hickey). In Proceedings of International Symposium on Algorithms and Computation (ISAAC), 2019.

Motivated by the growth in outsourced data analysis, we describe methods for verifying basic linear algebra operations performed by a cloud service without having to recalculate the entire result. We provide novel protocols in the streaming setting for inner product, matrix multiplication and vector-matrix-vector multiplication where the number of rounds of interaction can be adjusted to tradeoff space, communication, and duration of the protocol. Previous work suggests that the costs of these interactive protocols are optimized by choosing O(logn) rounds. However, we argue that we can reduce the number of rounds without incurring a significant time penalty by considering the total end-to-end time, so fewer rounds and larger messages are preferable. We confirm this claim with an experimental study that shows that a constant number of rounds gives the fastest protocol.

Towards a theory of parameterized streaming algorithms (R. Chitnis and G. Cormode). In International Symposium on Parameterized and Exact Computation, 2019.

Parameterized complexity attempts to give a more fine-grained analysis of the complexity of problems: instead of measuring the running time as a function of only the input size, we analyze the running time with respect to additional parameters. This approach has proven to be highly successful in delineating our understanding of NP-hard problems. Given this success with the TIME resource, it seems but natural to use this approach for dealing with the SPACE resource. First attempts in this direction have considered a few individual problems, with some success: Fafianie and Kratsch [MFCS’14] and Chitnis et al. [SODA’15] introduced the notions of streaming kernels and parameterized streaming algorithms respectively. For example, the latter shows how to refine the Ω(n²) bit lower bound for finding a minimum Vertex Cover (VC) in the streaming setting by designing an algorithm for the parameterized k-VC problem which uses O(k²logn) bits. In this paper, we initiate a systematic study of graph problems from the paradigm of parameterized streaming algorithms. We first define a natural hierarchy of space complexity classes of FPS, SubPS, SemiPS, SupPS and BPS, and then obtain tight classifications for several well-studied graph problems such as Longest Path, Feedback Vertex Set, Dominating Set, Girth, Treewidth, etc. into this hierarchy. On the algorithmic side, our parameterized streaming algorithms use techniques from the FPT world such as bidimensionality, iterative compression and bounded-depth search trees. On the hardness side, we obtain lower bounds for the parameterized streaming complexity of various problems via novel reductions from problems in communication complexity. We also show a general (unconditional) lower bound for space complexity of parameterized streaming algorithms for a large class of problems inspired by the recently developed frameworks for showing (conditional) kernelization lower bounds. Parameterized algorithms and streaming algorithms are approaches to cope with TIME and SPACE intractability respectively. It is our hope that this work on parameterized streaming algorithms leads to two-way flow of ideas between these two previously separated areas of theoretical computer science.