arXiv subjects

Jared Saia

Publications and source records attributed to Jared Saia.

32 records · Page 2Linked to original sources

arXiv2014

Secure Anonymous Broadcast

In anonymous broadcast, one or more parties want to anonymously send messages to all parties. This problem is increasingly important as a black-box in many privacy-preserving applications such as anonymous communication, distributed auctions, and multi-party computation. In this paper, we design decentralized protocols for anonymous broadcast that require each party to send (and compute) a polylogarithmic number of bits (and operations) per anonymous bit delivered with $O(\log n)$ rounds of communication. Our protocol is provably secure against traffic analysis, does not require any trusted party, and is completely load-balanced. The protocol tolerates up to $n/6$ statically-scheduled Byzantine parties that are controlled by a computationally unbounded adversary. Our main strategy for achieving scalability is to perform local communications (and computations) among a logarithmic number of parties. We provide simulation results to show that our protocol improves significantly over previous work. We finally show that using a common cryptographic tool in our protocol one can achieve practical results for anonymous broadcast.

cs.DC↗

arXiv2013

Quorums Quicken Queries: Efficient Asynchronous Secure Multiparty Computation

We describe an asynchronous algorithm to solve secure multiparty computation (MPC) over n players, when strictly less than a 1/8 fraction of the players are controlled by a static adversary. For any function f over a field that can be computed by a circuit with m gates, our algorithm requires each player to send a number of field elements and perform an amount of computation that is O (m/n + \sqrt{n}). This significantly improves over traditional algorithms, which require each player to both send a number of messages and perform computation that is Ω(nm). Additionally, we define the threshold counting problem and present a distributed algorithm to solve it in the asynchronous communication model. Our algorithm is load balanced, with computation, communication and latency complexity of O(log n), and may be of independent interest to other applications with a load balancing goal in mind.

cs.DS↗

arXiv2013

The Power of Mediation in an Extended El Farol Game

A mediator implements a correlated equilibrium when it proposes a strategy to each player confidentially such that the mediator's proposal is the best interest for every player to follow. In this paper, we present a mediator that implements the best correlated equilibrium for an extended El Farol game with symmetric players. The extended El Farol game we consider incorporates both negative and positive network effects. We study the degree to which this type of mediator can decrease the overall social cost. In particular, we give an exact characterization of Mediation Value (MV) and Enforcement Value (EV) for this game. MV is the ratio of the minimum social cost over all Nash equilibria to the minimum social cost over all mediators, and EV is the ratio of the minimum social cost over all mediators to the optimal social cost. This sort of exact characterization is uncommon for games with both kinds of network effects. An interesting outcome of our results is that both the MV and EV values can be unbounded for our game.

cs.GT↗

arXiv2013

Self-Healing Algorithms of Byzantine Faults

Recent years have seen significant interest in designing networks that are self-healing in the sense that they can automatically recover from adversarial attacks. Previous work shows that it is possible for a network to automatically recover, even when an adversary repeatedly deletes nodes in the network. However, there have not yet been any algorithms that self-heal in the case where an adversary takes over nodes in the network. In this paper, we address this gap. In particular, we describe a communication network over n nodes that ensures the following properties, even when an adversary controls up to t <= (1/8 - ε)n nodes, for any non-negative ε. First, the network provides a point-to-point communication with bandwidth and latency costs that are asymptotically optimal. Second, the expected total number of message corruptions is O(t(log* n)^2) before the adversarially controlled nodes are effectively quarantined so that they cause no more corruptions. Empirical results show that our algorithm can reduce the bandwidth cost by up to a factor of 70.

cs.CR↗

arXiv2012

Scalable Mechanisms for Rational Secret Sharing

We consider the classical secret sharing problem in the case where all agents are selfish but rational. In recent work, Kol and Naor show that, when there are two players, in the non-simultaneous communication model, i.e. when rushing is possible, there is no Nash equilibrium that ensures both players learn the secret. However, they describe a mechanism for this problem, for any number of players, that is an epsilon-Nash equilibrium, in that no player can gain more than epsilon utility by deviating from it. Unfortunately, the Kol and Naor mechanism, and, to the best of our knowledge, all previous mechanisms for this problem require each agent to send O(n) messages in expectation, where n is the number of agents. This may be problematic for some applications of rational secret sharing such as secure multi-party computation and simulation of a mediator. We address this issue by describing mechanisms for rational secret sharing that are designed for large n. Both of our results hold for n > 2, and are Nash equilbria, rather than just epsilon-Nash equilbria. Our first result is a mechanism for n-out-of-n rational secret sharing that is scalable in the sense that it requires each agent to send only an expected O(log n) bits. Moreover, the latency of this mechanism is O(log n) in expectation, compared to O(n) expected latency for the Kol and Naor result. Our second result is a mechanism for a relaxed variant of rational m-out-of-n secret sharing where m = Theta(n). It requires each processor to send O(log n) bits and has O(log n) latency. Both of our mechanisms are non-cryptographic, and are not susceptible to backwards induction.

cs.DS↗

arXiv2010

Combinatorial Auctions with Budgets

We consider budget constrained combinatorial auctions where bidder $i$ has a private value $v_i$, a budget $b_i$, and is interested in all the items in $S_i$. The value to agent $i$ of a set of items $R$ is $|R \cap S_i| \cdot v_i$. Such auctions capture adword auctions, where advertisers offer a bid for ads in response to an advertiser-dependent set of adwords, and advertisers have budgets. It is known that even of all items are identical and all budgets are public it is not possible to be truthful and efficient. Our main result is a novel auction that runs in polynomial time, is incentive compatible, and ensures Pareto-optimality for such auctions when the valuations are private and the budgets are public knowledge. This extends the result of Dobzinski et al. (FOCS 2008) for auctions of multiple {\sl identical} items and public budgets to single-valued {\sl combinatorial} auctions with public budgets.

cs.GT↗

arXiv2010

Breaking the O(n^2) Bit Barrier: Scalable Byzantine agreement with an Adaptive Adversary

We describe an algorithm for Byzantine agreement that is scalable in the sense that each processor sends only $\tilde{O}(\sqrt{n})$ bits, where $n$ is the total number of processors. Our algorithm succeeds with high probability against an \emph{adaptive adversary}, which can take over processors at any time during the protocol, up to the point of taking over arbitrarily close to a 1/3 fraction. We assume synchronous communication but a \emph{rushing} adversary. Moreover, our algorithm works in the presence of flooding: processors controlled by the adversary can send out any number of messages. We assume the existence of private channels between all pairs of processors but make no other cryptographic assumptions. Finally, our algorithm has latency that is polylogarithmic in $n$. To the best of our knowledge, ours is the first algorithm to solve Byzantine agreement against an adaptive adversary, while requiring $o(n^{2})$ total bits of communication.

cs.DC↗

arXiv2009

Improved Approximation Algorithms for Segment Minimization in Intensity Modulated Radiation Therapy

he segment minimization problem consists of finding the smallest set of integer matrices that sum to a given intensity matrix, such that each summand has only one non-zero value, and the non-zeroes in each row are consecutive. This has direct applications in intensity-modulated radiation therapy, an effective form of cancer treatment. We develop three approximation algorithms for matrices with arbitrarily many rows. Our first two algorithms improve the approximation factor from the previous best of $1+\log_2 h $ to (roughly) $3/2 \cdot (1+\log_3 h)$ and $11/6\cdot(1+\log_4{h})$, respectively, where $h$ is the largest entry in the intensity matrix. We illustrate the limitations of the specific approach used to obtain these two algorithms by proving a lower bound of $\frac{(2b-2)}{b}\cdot\log_b{h} + \frac{1}{b}$ on the approximation guarantee. Our third algorithm improves the approximation factor from $2 \cdot (\log D+1)$ to $24/13 \cdot (\log D+1)$, where $D$ is (roughly) the largest difference between consecutive elements of a row of the intensity matrix. Finally, experimentation with these algorithms shows that they perform well with respect to the optimum and outperform other approximation algorithms on 77% of the 122 test cases we consider, which include both real world and synthetic data.

cs.DS↗

arXiv2009

The Forgiving Graph: A distributed data structure for low stretch under adversarial attack

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that, over a sequence of rounds, an adversary either inserts a node with arbitrary connections or deletes an arbitrary node from the network. The network responds to each such change by quick "repairs," which consist of adding or deleting a small number of edges. These repairs essentially preserve closeness of nodes after adversarial deletions, without increasing node degrees by too much, in the following sense. At any point in the algorithm, nodes $v$ and $w$ whose distance would have been $\ell$ in the graph formed by considering only the adversarial insertions (not the adversarial deletions), will be at distance at most $\ell \log n$ in the actual graph, where $n$ is the total number of vertices seen so far. Similarly, at any point, a node $v$ whose degree would have been $d$ in the graph with adversarial insertions only, will have degree at most 3d in the actual graph. Our algorithm is completely distributed and has low latency and bandwidth requirements.

cs.DS↗

arXiv2008

The Forgiving Tree: A Self-Healing Distributed Data Structure

We consider the problem of self-healing in peer-to-peer networks that are under repeated attack by an omniscient adversary. We assume that the following process continues for up to n rounds where n is the total number of nodes initially in the network: the adversary deletes an arbitrary node from the network, then the network responds by quickly adding a small number of new edges. We present a distributed data structure that ensures two key properties. First, the diameter of the network is never more than $O(\log Δ)$ times its original diameter, where $Δ$ is the maximum degree of the network initially. We note that for many peer-to-peer systems, $Δ$ is polylogarithmic, so the diameter increase would be a O(log log n) multiplicative factor. Second, the degree of any node never increases by more than 3 over its original degree. Our data structure is fully distributed, has O(1) latency per round and requires each node to send and receive O(1) messages per round. The data structure requires an initial setup phase that has latency equal to the diameter of the original network, and requires, with high probability, each node v to send O(log n) messages along every edge incident to v. Our approach is orthogonal and complementary to traditional topology-based approaches to defending against attack.

cs.DC↗

arXiv2008

Picking up the Pieces: Self-Healing in Reconfigurable Networks

We consider the problem of self-healing in networks that are reconfigurable in the sense that they can change their topology during an attack. Our goal is to maintain connectivity in these networks, even in the presence of repeated adversarial node deletion, by carefully adding edges after each attack. We present a new algorithm, DASH, that provably ensures that: 1) the network stays connected even if an adversary deletes up to all nodes in the network; and 2) no node ever increases its degree by more than 2 log n, where n is the number of nodes initially in the network. DASH is fully distributed; adds new edges only among neighbors of deleted nodes; and has average latency and bandwidth costs that are at most logarithmic in n. DASH has these properties irrespective of the topology of the initial network, and is thus orthogonal and complementary to traditional topology-based approaches to defending against attack. We also prove lower-bounds showing that DASH is asymptotically optimal in terms of minimizing maximum degree increase over multiple attacks. Finally, we present empirical results on power-law graphs that show that DASH performs well in practice, and that it significantly outperforms naive algorithms in reducing maximum degree increase. We also present empirical results on performance of our algorithms and a new heuristic with regard to stretch (increase in shortest path lengths).

cs.DS↗

arXiv2007

Sleeping on the Job: Energy-Efficient Broadcast for Radio Networks

We address the problem of minimizing power consumption when performing reliable broadcast on a radio network under the following popular model. Each node in the network is located on a point in a two dimensional grid, and whenever a node sends a message, all awake nodes within distance r receive the message. In the broadcast problem, some node wants to successfully send a message to all other nodes in the network even when up to a 1/2 fraction of the nodes within every neighborhood can be deleted by an adversary. The set of deleted nodes is carefully chosen by the adversary to foil our algorithm and moreover, the set of deleted nodes may change periodically. This models worst-case behavior due to mobile nodes, static nodes losing power or simply some points in the grid being unoccupied. A trivial solution requires each node in the network to be awake roughly 1/2 the time, and a trivial lower bound shows that each node must be awake for at least a 1/n fraction of the time. Our first result is an algorithm that requires each node to be awake for only a 1/sqrt(n) fraction of the time in expectation. Our algorithm achieves this while ensuring correctness with probability 1, and keeping optimal values for other resource costs such as latency and number of messages sent. We give a lower-bound that shows that this reduction in power consumption is asymptotically optimal when latency and number of messages sent must be optimal. If we can increase the latency and messages sent by only a log*n factor we give a Las Vegas algorithm that requires each node to be awake for only a (log*n)/n expected fraction of the time; we give a lower-bound showing that this second algorithm is near optimal. Finally, we show how to ensure energy-efficient broadcast in the presence of Byzantine faults.

cs.DS↗

arXiv2005

The Impact of Social Networks on Multi-Agent Recommender Systems

Awerbuch et al.'s approach to distributed recommender systems (DRSs) is to have agents sample products at random while randomly querying one another for the best item they have found; we improve upon this by adding a communication network. Agents can only communicate with their immediate neighbors in the network, but neighboring agents may or may not represent users with common interests. We define two network structures: in the ``mailing-list model,'' agents representing similar users form cliques, while in the ``word-of-mouth model'' the agents are distributed randomly in a scale-free network (SFN). In both models, agents tell their neighbors about satisfactory products as they are found. In the word-of-mouth model, knowledge of items propagates only through interested agents, and the SFN parameters affect the system's performance. We include a summary of our new results on the character and parameters of random subgraphs of SFNs, in particular SFNs with power-law degree distributions down to minimum degree 1. These networks are not as resilient as Cohen et al. originally suggested. In the case of the widely-cited ``Internet resilience'' result, high failure rates actually lead to the orphaning of half of the surviving nodes after 60% of the network has failed and the complete disintegration of the network at 90%. We show that given an appropriate network, the communication network reduces the number of sampled items, the number of messages sent, and the amount of ``spam.'' We conclude that in many cases DRSs will be useful for sharing information in a multi-agent learning system.

cs.LG↗

arXiv2005

Parameters Affecting the Resilience of Scale-Free Networks to Random Failures

It is commonly believed that scale-free networks are robust to massive numbers of random node deletions. For example, Cohen et al. study scale-free networks including some which approximate the measured degree distribution of the Internet. Their results suggest that if each node in this network failed independently with probability 0.99, the remaining network would continue to have a giant component. In this paper, we show that a large and important subclass of scale-free networks are not robust to massive numbers of random node deletions for practical purposes. In particular, we study finite scale-free networks which have minimum node degree of 1 and a power-law degree distribution beginning with nodes of degree 1 (power-law networks). We show that, in a power-law network approximating the Internet's reported distribution, when the probability of deletion of each node is 0.5 only about 25% of the surviving nodes in the network remain connected in a giant component, and the giant component does not persist beyond a critical failure rate of 0.9. The new result is partially due to improved analytical accommodation of the large number of degree-0 nodes that result after node deletions. Our results apply to finite power-law networks with a wide range of power-law exponents, including Internet-like networks. We give both analytical and empirical evidence that such networks are not generally robust to massive random node deletions.

cs.NI↗