Bloom Filters in Adversarial Environments

Many efficient data structures use randomness, allowing them to improve upon deterministic ones. Usually, their efficiency and correctness are analyzed using probabilistic tools under the assumption that the inputs and queries are independent of the internal randomness of the data structure. In this work, we consider data structures in a more robust model, which we call the adversarial model. Roughly speaking, this model allows an adversary to choose inputs and queries adaptively according to previous responses. Specifically, we consider a data structure known as "Bloom filter" and prove a tight connection between Bloom filters in this model and cryptography. A Bloom filter represents a set $S$ of elements approximately, by using fewer bits than a precise representation. The price for succinctness is allowing some errors: for any $x \in S$ it should always answer `Yes', and for any $x \notin S$ it should answer `Yes' only with small probability. In the adversarial model, we consider both efficient adversaries (that run in polynomial time) and computationally unbounded adversaries that are only bounded in the number of queries they can make. For computationally bounded adversaries, we show that non-trivial (memory-wise) Bloom filters exist if and only if one-way functions exist. For unbounded adversaries we show that there exists a Bloom filter for sets of size $n$ and error $\varepsilon$, that is secure against $t$ queries and uses only $O(n \log{\frac{1}{\varepsilon}}+t)$ bits of memory. In comparison, $n\log{\frac{1}{\varepsilon}}$ is the best possible under a non-adaptive adversary

The Journey from NP to TFNP Hardness

The class TFNP is the search analog of NP with the additional guarantee that any instance has a solution. TFNP has attracted extensive attention due to its natural syntactic subclasses that capture the computational complexity of important search problems from algorithmic game theory, combinatorial optimization and computational topology. Thus, one of the main research objectives in the context of TFNP is to search for efficient algorithms for its subclasses, and at the same time proving hardness results where efficient algorithms cannot exist. Currently, no problem in TFNP is known to be hard under assumptions such as NP hardness, the existence of one-way functions, or even public-key cryptography. The only known hardness results are based on less general assumptions such as the existence of collision-resistant hash functions, one-way permutations less established cryptographic primitives (e.g. program obfuscation or functional encryption). Several works explained this status by showing various barriers to proving hardness of TFNP. In particular, it has been shown that hardness of TFNP hardness cannot be based on worst-case NP hardness, unless NP=coNP. Therefore, we ask the following question: What is the weakest assumption sufficient for showing hardness in TFNP? In this work, we answer this question and show that hard-on-average TFNP problems can be based on the weak assumption that there exists a hard-on-average language in NP. In particular, this includes the assumption of the existence of one-way functions. In terms of techniques, we show an interesting interplay between problems in TFNP, derandomization techniques, and zero-knowledge proofs

Congested Clique Algorithms for Graph Spanners

Graph spanners are sparse subgraphs that faithfully preserve the distances in the original graph up to small stretch. Spanner have been studied extensively as they have a wide range of applications ranging from distance oracles, labeling schemes and routing to solving linear systems and spectral sparsification. A k-spanner maintains pairwise distances up to multiplicative factor of k. It is a folklore that for every n-vertex graph G, one can construct a (2k-1) spanner with O(n^{1+1/k}) edges. In a distributed setting, such spanners can be constructed in the standard CONGEST model using O(k^2) rounds, when randomization is allowed. In this work, we consider spanner constructions in the congested clique model, and show: - a randomized construction of a (2k-1)-spanner with O~(n^{1+1/k}) edges in O(log k) rounds. The previous best algorithm runs in O(k) rounds; - a deterministic construction of a (2k-1)-spanner with O~(n^{1+1/k}) edges in O(log k +(log log n)^3) rounds. The previous best algorithm runs in O(k log n) rounds. This improvement is achieved by a new derandomization theorem for hitting sets which might be of independent interest; - a deterministic construction of a O(k)-spanner with O(k * n^{1+1/k}) edges in O(log k) rounds

Secure Distributed Network Optimization Against Eavesdroppers

We present a new algorithmic framework for distributed network optimization in the presence of eavesdropper adversaries, also known as passive wiretappers. In this setting, the adversary is listening to the traffic exchanged over a fixed set of edges in the graph, trying to extract information on the private input and output of the vertices. A distributed algorithm is denoted as f-secure, if it guarantees that the adversary learns nothing on the input and output for the vertices, provided that it controls at most f graph edges. Recent work has presented general simulation results for f-secure algorithms, with a round overhead of D^?(f), where D is the diameter of the graph. In this paper, we present a completely different white-box, and yet quite general, approach for obtaining f-secure algorithms for fundamental network optimization tasks. Specifically, for n-vertex D-diameter graphs with (unweighted) edge-connectivity ?(f), there are f-secure congest algorithms for computing MST, partwise aggregation, and (1+?) (weighted) minimum cut approximation, within O?(D+f ?n) congest rounds, hence nearly tight for f = O?(1). Our algorithms are based on designing a secure algorithmic-toolkit that leverages the special structure of congest algorithms for global optimization graph problems. One of these tools is a general secure compiler that simulates light-weight distributed algorithms in a congestion-sensitive manner. We believe that these tools set the ground for designing additional secure solutions in the congest model and beyond

Tight Security Bounds for Micali’s SNARGs

Succinct non-interactive arguments (SNARGs) in the random oracle model (ROM) have several attractive features: they are plausibly post-quantum; they can be heuristically instantiated via lightweight cryptography; and they have a transparent (public-coin) parameter setup. The canonical construction of a SNARG in the ROM is due to Micali (FOCS 1994), who showed how to use a random oracle to compile any probabilistically checkable proof (PCP) with sufficiently-small soundness error into a corresponding SNARG. Yet, while Micali\u27s construction is a seminal result, it has received little attention in terms of analysis in the past 25 years. In this paper, we observe that prior analyses of the Micali construction are not tight and then present a new analysis that achieves tight security bounds. Our result enables reducing the random oracle\u27s output size, and obtain corresponding savings in concrete argument size. Departing from prior work, our approach relies on precisely quantifying the cost for an attacker to find several collisions and inversions in the random oracle, and proving that any PCP with small soundness error withstands attackers that succeed in finding a small number of collisions and inversions in a certain tree-based information-theoretic game