Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userFree2Shard: Adaptive-adversary-resistant sharding via Dynamic Self Allocation
107
0
0.0
(
0
)
Added by
Ranvir Rana
Publication date
2020
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Propelled by the growth of large-scale blockchain deployments, much recent progress has been made in designing sharding protocols that achieve throughput scaling linearly in the number of nodes. However, existing protocols are not robust to an adversary adaptively corrupting a fixed fraction of nodes. In this paper, we propose Free2Shard -- a new architecture that achieves near-linear scaling while being secure against a fully adaptive adversary. The focal point of this architecture is a dynamic self-allocation algorithm that lets users allocate themselves to shards in response to adversarial action, without requiring a central or cryptographic proof. This architecture has several attractive features unusual for sharding protocols, including: (a) the ability to handle the regime of large number of shards (relative to the number of nodes); (b) heterogeneous shard demands; (c) requiring only a small minority to follow the self-allocation; (d) asynchronous shard rotation; (e) operation in a purely identity-free proof-of-work setting. The key technical contribution is a deep mathematical connection to the classical work of Blackwell in dynamic game theory.
rate research
Read More
Blockchain is an incrementally updated ledger maintained by distributed nodes rather than centralized organizations. The current blockchain technology faces scalability issues, which include two aspects: low transaction throughput and high storage capacity costs. This paper studies the blockchain structure based on state sharding technology, and mainly solves the problem of non-scalability of block chain storage. This paper designs and implements the blockchain state sharding scheme, proposes a specific state sharding data structure and algorithm implementation, and realizes a complete blockchain structure so that the blockchain has the advantages of high throughput, processing a large number of transactions and saving storage costs. Experimental results show that a blockchain network with more than 100,000 nodes can be divided into 1024 shards. A blockchain network with this structure can process 500,000 transactions in about 5 seconds. If the consensus time of the blockchain is about 10 seconds, and the block generation time of the blockchain system of the sharding mechanism is 15 seconds, the transaction throughput can reach 33,000 tx/sec. Experimental results show that the throughput of the proposed protocol increases with the increase of the network node size. This confirms the scalability of the blockchain structure based on sharding technology.
We present a new dynamic matching sparsification scheme. From this scheme we derive a framework for dynamically rounding fractional matchings against emph{adaptive adversaries}. Plugging in known dynamic fractional matching algorithms into our framework, we obtain numerous randomized dynamic matching algorithms which work against adaptive adversaries (the first such algorithms, as all previous randomized algorithms for this problem assumed an emph{oblivious} adversary). In particular, for any constant $epsilon>0$, our framework yields $(2+epsilon)$-approximate algorithms with constant update time or polylog worst-case update time, as well as $(2-delta)$-approximate algorithms in bipartite graphs with arbitrarily-small polynomial update time, with all these algorithms guarantees holding against adaptive adversaries. All these results achieve emph{polynomially} better update time to approximation tradeoffs than previously known to be achievable against adaptive adversaries.
Designing dynamic graph algorithms against an adaptive adversary is a major goal in the field of dynamic graph algorithms. While a few such algorithms are known for spanning trees, matchings, and single-source shortest paths, very little was known for an important primitive like graph sparsifiers. The challenge is how to approximately preserve so much information about the graph (e.g., all-pairs distances and all cuts) without revealing the algorithms underlying randomness to the adaptive adversary. In this paper we present the first non-trivial efficient adaptive algorithms for maintaining spanners and cut sparisifers. These algorithms in turn imply improvements over existing algorithms for other problems. Our first algorithm maintains a polylog$(n)$-spanner of size $tilde O(n)$ in polylog$(n)$ amortized update time. The second algorithm maintains an $O(k)$-approximate cut sparsifier of size $tilde O(n)$ in $tilde O(n^{1/k})$ amortized update time, for any $kge1$, which is polylog$(n)$ time when $k=log(n)$. The third algorithm maintains a polylog$(n)$-approximate spectral sparsifier in polylog$(n)$ amortized update time. The amortized update time of both algorithms can be made worst-case by paying some sub-polynomial factors. Prior to our result, there were near-optimal algorithms against oblivious adversaries (e.g. Baswana et al. [TALG12] and Abraham et al. [FOCS16]), but the only non-trivial adaptive dynamic algorithm requires $O(n)$ amortized update time to maintain $3$- and $5$-spanner of size $O(n^{1+1/2})$ and $O(n^{1+1/3})$, respectively [Ausiello et al. ESA05]. Our results are based on two novel techniques. The first technique, is a generic black-box reduction that allows us to assume that the graph undergoes only edge deletions and, more importantly, remains an expander with almost-uniform degree. The second technique we call proactive resampling. [...]
Caching and rate allocation are two promising approaches to support video streaming over wireless network. However, existing rate allocation designs do not fully exploit the advantages of the two approaches. This paper investigates the problem of cache-enabled QoE-driven video rate allocation problem. We establish a mathematical model for this problem, and point out that it is difficult to solve the problem with traditional dynamic programming. Then we propose a deep reinforcement learning approaches to solve it. First, we model the problem as a Markov decision problem. Then we present a deep Q-learning algorithm with a special knowledge transfer process to find out effective allocation policy. Finally, numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality user experience of mobile user moving among small cells. We also investigate the impact of configuration of critical parameters on the performance of our algorithm.
Imagine that a malicious hacker is trying to attack a server over the Internet and the server wants to block the attack packets as close to their point of origin as possible. However, the security gateway ahead of the source of attack is untrusted. How can the server block the attack packets through this gateway? In this paper, we introduce REMOTEGATE, a trustworthy mechanism for allowing any party (server) on the Internet to configure a security gateway owned by a second party, at a certain agreed upon reward that the former pays to the latter for its service. We take an interactive incentive-compatible approach, for the case when both the server and the gateway are rational, to devise a protocol that will allow the server to help the security gateway generate and deploy a policy rule that filters the attack packets before they reach the server. The server will reward the gateway only when the latter can successfully verify that it has generated and deployed the correct rule for the issue. This mechanism will enable an Internet-scale approach to improving security and privacy, backed by digital payment incentives.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
