Current mainstream anonymous communication systems face a fundamental trade-off: onion routing’s long-lived circuits are vulnerable to traffic analysis, while mixnets’ reliance on a global network knowledge hinders scalability. We propose Draughts, a system that resolves this dilemma through a decentralized, hop-by-hop routing model. Extending Jump routing, Draughts’s key innovations are twofold: it replaces global state dependency with local two-hop neighbor knowledge, and enables anonymous replies by embedding a commutatively encrypted initiator address in forward packets. Our evaluation shows that Draughts achieves sender anonymity on par with global-knowledge systems and remains robust against global passive traffic analysis, with latency and anonymity stable as the network scales, affirming its viability for large-scale, interactive applications.

As the most popular decentralized service architecture, the availability of InterPlanetary File System(IPFS) for content fetch and function computation services is constrained by inefficient retrieval and rigid routing mechanisms. As a representative of new-generation networks, Information-centric networking (ICN) achieves fast content location and flexible routing capabilities, making ICN a crucial method to optimize IPFS. However, existing methods lack protocols and identifiers matching mechanisms between IPFS and ICN, making compatibility with the IP-based IPFS protocol stack and IPFS peers challenging. Therefore, we propose a new IPFS service architecture compatible with locator/identifier separation ICN, HPIPFS, which leverages network devices' growing computing and storage capabilities to integrate ICN edge nodes with IPFS peers at the application layer. HP-IPFS converts IPFS requests into ICN requests at ICN edge nodes, thereby bridging ICN resources with IP-based IPFS peers. Simultaneously, IPFS requests can utilize ICN's fast content location capabilities and flexible routing mechanisms to reduce content retrieval latency and improve the routing efficiency of computation requests. In different topology scales, experimental results demonstrate that compared to the original IPFS architecture, HP-IPFS achieved a maximum reduction of 34.26% in Time-To-First-Block and 34.31% in data block fetch latency. Meanwhile, HP-IPFS increased the computing task completion rate and computing resource utilization by 41.35% and 43.5%. At the same time, HP-IPFS demonstrates significant performance advantages in simulations at large realworld network scale.

Eavesdropping attacks, a common network threat, can lead to data leakage and more severe attacks. Current static defense techniques are inadequate in dealing with persistent, adaptive attackers. Moving Target Defense (MTD) offers a proactive solution by dynamically altering network parameters, increasing the cost for attackers. This paper proposes a K-Randomized Routing Dispersion (KRRD) overlay architecture for eavesdropping defense, which enhances security by making it more difficult for attackers to intercept and analyze data. This approach dynamically generates candidate node sets based on the independence of underlying physical links. By achieving packet-level randomized routing through KRRD, it becomes fundamentally difficult for attackers to identify the nodes of the routing paths, preventing them from intercepting complete data. Furthermore, Information-Centric Networking (ICN) technology is incorporated into the MTD framework, employing identifier-based addressing and transmission to conceal the actual endpoint addresses. Combined with Software-Defined Networking (SDN) for hop-by-hop address rewriting, this strategy further confuses attackers' assessments. Various experiments demonstrate that, compared to classical routing mutation schemes, our method reduces the packet eavesdropping rate by up to 80 %, while also exhibiting good scalability and usability.

The issue of multi-set membership query is a fundamental task in the fields of distributed systems and computer networks. It entails identifying which sets, out of n sets$S_{0}, S_{1},{\ldots }, S_{n-1}$, in a Multi Set Multi-Membership Querying (MS-MMQ) contain a given element q. To address this problem while minimizing space usage, probabilistic data structures, such as cuckoo filters, are commonly employed. However, existing sketch data structures struggle to effectively balance scalability and query efficiency. To address this challenge, we introduce an enhanced marked cuckoo filter (EMCF), which enhances support for MS-MMQ scenarios by incorporating set markers after the fingerprint field. Additionally, it grants horizontal scalability and collaborative search capabilities at the filter level through the inclusion of global markers. Furthermore, we have developed an optimized variant of the enhanced marked cuckoo filter (EMCF-V) for multi-set scenarios to achieve space optimization. Experimental results using real-world datasets demonstrate that EMCF exceeds CSC-CF by more than 10 times in terms of speed, and EMCF-V exhibits a 33.3% higher query efficiency than CSC-CF methods, particularly in multiset scenarios.

Information-Centric Networking (ICN) separates the identifier and address of the host, which enables the multi-homed host to own multiple addresses. The packet carrying multiple destination addresses could obtain multiple output ports after matching the routing table. Therefore, in order to select the best output port for the packet with multiple destination addresses, the router needs to maintain a status table to record the dynamic status values of different ports. Since the status table directs the forwarding of the flow, the accuracy of the status value is crucial to the performance of the transmission. In the paper, we propose an improved maximum flow routing algorithm to calculate the status value of the status table to improve the efficiency of the transmission. The proposed algorithm utilizes the dynamic network information to maximize the transmission rate of the flow. Besides, status table is also dynamically updated to adapt to the change of the flows in the network. Experimental results show this routing algorithm could increase the throughput by up to 40% and decrease the flow completion time by about 30%. The maximum flow routing algorithm achieves good performance even in heterogeneous path environment and high network load. The dynamic update of the status table could also improve the routing robustness of the network.

Video services are flourishing, and the same content might be hosted by different Web sites. A user typically does not care about which provider provides the service to him/her but rather the quality of the service. However, the network condition is not stable and it is hard to obtain the service performance in real time during the online service. Therefore, it is very important to understand the potential characteristics of video services and design the predictors to forecast the service performance. In our work, we design a measurement system deployed in 11 provinces and cities in China, monitor and analyze two popular Web sites, Youku and Tudou. Based on the analysis of the measured data, we see that the performance trend of two service providers has the diurnal pattern, and the worst performance is typically during the prime time, 8 - 9 pm. To understand the service structure of the website, the underlying CDN architecture is studied and the performance impact factors are analyzed. For performance forecasting, a modified time series model is proposed and evaluated, which shows that it can obtain obvious better performance than baseline models. Moreover, a new predictor by combining different information sources is designed, which can improve the forecasting precision significantly, and it may be useful in service source analyzing and recommendation systems in the future.

To predict the availability state of a node in a distribution network, its history trace is usually used. Sometimes, some usage behavior patterns cannot be captured precisely from the insufficient trace, which may lead to unreliable predictors. In this paper, to alleviate the data sparseness problem, the nodes with the similar behaviors are clustered, and all history information in a same cluster is seen as another information source for any node in it. For each node, an N-gram model is used to train the predictor by the combination of the new source and the node's own trace. In addition, because it is hard to capture the trace of all nodes in large scale networks, such as P2P networks, a bagging based prediction algorithm is proposed, which can be applied in the distribution environment and relieve the effect of the noisy data. In our experiments, three datasets are evaluated. Results show that the prediction performance of our cluster based N-gram predictor is better than the results of several other predictors. And the bagging based prediction algorithm presents its validity in the distribution environment.

In this paper, we propose to use discriminative training (DT) for improving letter-to-sound (LTS) conversion performance. LTS is a critical component in both ASR and TTS for predicting the correct pronunciation of a word not included in the lexicon. For TTS applications, predicting the proper pronunciation of an out-of-vocabulary person/place name, especially a name with foreign origin can be challenging. We utilize discriminative training, which has been successfully used in speech recognition, to sharpen the baseline N-grams of grapheme-phoneme pairs. We address the problem in a unified framework of discriminative training. Two criteria, maximum mutual information (MMI) and minimum phoneme error (MPE), are investigated. Experimental results show that DT yields a small (3.8-4.6% relative) but consistent error reduction across all databases tested. In addition, we observe that by pinpointing the local errors in a finer resolution, we can obtain a better discriminative model.

We propose a novel way to alleviate the data sparseness problem in training letter-to-sound (LTS) N-gram models by adding automatically generated new words to the training set. The proposed method consists of two procedures: (1) generating a large pool of new words automatically; (2) selecting good new word candidates from the new word pool via semi-supervised learning. The new words are created by replacing stressed syllables of an existing word with other stressed syllables under specified contextual constraints. The new word selection by semi-supervised learning is based upon consistent pronunciation predictions by different LTS models. After adding new words to the training set, the performance of LTS conversion is significantly improved. For the NetTalk dictionary, compared with the performance from the N-gram baseline model, 21.6% relative word error rate reduction is obtained. For the CMU dictionary, 9.1% and 5.6% relative word error rate reductions are obtained, respectively, with/without considering the stress.

To identify the language origin of a named entity, morphological information associated with its letter spelling, such as letter N-grams, is commonly employed. However, with this information only, named entities with similar spellings but from different language origins are difficult to differentiate. In this paper, a measure of "popularity," in terms of frequency or page count of the named entity in language-specific Web search, is proposed for identifying its language origin. Morphological information, including letter or letter-chunk N-grams, is used to enhance the performance of language identification in conjunction with Web-based page counts. Six languages, including English, German, French, Portuguese, Chinese, and Japanese (Chinese and Japanese named entities are shown in their corresponding phonetic alphabets, i.e., Pinyin and Romaji), are tested. Experiments show that when classifying four Latin languages, including English, German, French, and Portuguese, which are written in Latin alphabets, features from different information sources yield substantial performance improvements in the classification accuracy over a letter 4-gram-based baseline system. The accuracy increases from 75.0% to 86.3%, or a 45.2% relative error reduction.

Identifying the language origin of a name in English is important for generating its correct pronunciation. In this paper, N-grams of syllable-based letter clusters are proposed for the task. The performance of the N-gram model of a set of frequently used letter clusters (correspond to syllables) is compared to that of letter N-gram model in a four-language task: English, German, French, and Portuguese. On average, the letter cluster N-gram, which has 26% error rate, is slightly better than the letter N-gram, which has 27.2% error rate. Furthermore, it is found that the error distributions from the two N-grams have fairly large differences. Therefore, AdaBoost is used to combine the results from N-grams of different units. The error rate is reduced to 22.5% or a relative 17.5% error reduction is achieved after the combination