Wenting Li

Wenting Li, Jiahong Xue, Xi Zhang et al.

Word embedding has become ubiquitous and is widely used in various natural language processing (NLP) tasks, such as web retrieval, web semantic analysis, and machine translation, and so on. Unfortunately, training the word embedding in a relatively large corpus is prohibitively expensive. We propose a graph-based word embedding algorithm, called Word-Graph2vec, which converts the large corpus into a word co-occurrence graph, then takes the word sequence samples from this graph by randomly traveling and trains the word embedding on this sampling corpus in the end. We posit that because of the limited vocabulary, huge idioms, and fixed expressions in English, the size and density of the word co-occurrence graph change slightly with the increase in the training corpus. So that Word-Graph2vec has stable runtime on the large-scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real-world datasets show that the proposed algorithm outperforms traditional Word2vec four to five times in terms of efficiency and two to three times than FastText, while the error generated by the random walk technique is small.

Michael Rodler, Wenting Li, Ghassan O. Karame et al.

Recent attacks exploiting errors in smart contract code had devastating consequences thereby questioning the benefits of this technology. It is currently highly challenging to fix errors and deploy a patched contract in time. Instant patching is especially important since smart contracts are always online due to the distributed nature of blockchain systems. They also manage considerable amounts of assets, which are at risk and often beyond recovery after an attack. Existing solutions to upgrade smart contracts depend on manual and error-prone processes. This paper presents a framework, called EVMPatch, to instantly and automatically patch faulty smart contracts. EVMPatch features a bytecode rewriting engine for the popular Ethereum blockchain, and transparently/automatically rewrites common off-the-shelf contracts to upgradable contracts. The proof-of-concept implementation of EVMPatch automatically hardens smart contracts that are vulnerable to integer over/underflows and access control errors, but can be easily extended to cover more bug classes. Our extensive evaluation on 14,000 real-world (vulnerable) contracts demonstrate that our approach successfully blocks attack transactions launched on these contracts, while keeping the intended functionality of the contract intact. We perform a study with experienced software developers, showing that EVMPatch is practical, and reduces the time for converting a given Solidity smart contract to an upgradable contract by 97.6 %, while ensuring functional equivalence to the original contract.

Michael Rodler, Wenting Li, Ghassan O. Karame et al.

Recently, a number of existing blockchain systems have witnessed major bugs and vulnerabilities within smart contracts. Although the literature features a number of proposals for securing smart contracts, these proposals mostly focus on proving the correctness or absence of a certain type of vulnerability within a contract, but cannot protect deployed (legacy) contracts from being exploited. In this paper, we address this problem in the context of re-entrancy exploits and propose a novel smart contract security technology, dubbed Sereum (Secure Ethereum), which protects existing, deployed contracts against re-entrancy attacks in a backwards compatible way based on run-time monitoring and validation. Sereum does neither require any modification nor any semantic knowledge of existing contracts. By means of implementation and evaluation using the Ethereum blockchain, we show that Sereum covers the actual execution flow of a smart contract to accurately detect and prevent attacks with a false positive rate as small as 0.06% and with negligible run-time overhead. As a by-product, we develop three advanced re-entrancy attacks to demonstrate the limitations of existing offline vulnerability analysis tools.

Claudio Soriente, Ghassan Karame, Wenting Li et al.

With the proliferation of Trusted Execution Environments (TEEs) such as Intel SGX, a number of cloud providers will soon introduce TEE capabilities within their offering (e.g., Microsoft Azure). Although the integration of SGX within the cloud considerably strengthens the threat model for cloud applications, the current model to deploy and provision enclaves prevents the cloud operator from adding or removing enclaves dynamically - thus preventing elasticity for TEE-based applications in the cloud. In this paper, we propose ReplicaTEE, a solution that enables seamless provisioning and decommissioning of TEE-based applications in the cloud. ReplicaTEE leverages an SGX-based provisioning layer that interfaces with a Byzantine Fault-Tolerant storage service to securely orchestrate enclave replication in the cloud, without the active intervention of the application owner. Namely, in ReplicaTEE, the application owner entrusts application secret to the provisioning layer; the latter handles all enclave commissioning and de-commissioning operations throughout the application lifetime. We analyze the security of ReplicaTEE and show that it is secure against attacks by a powerful adversary that can compromise a large fraction of the cloud infrastructure. We implement a prototype of ReplicaTEE in a realistic cloud environment and evaluate its performance. ReplicaTEE moderately increments the TCB by ~800 LoC. Our evaluation shows that ReplicaTEE does not add significant overhead to existing SGX-based applications.

Jian Liu, Wenting Li, Ghassan O. Karame et al.

Motivated by the great success and adoption of Bitcoin, a number of cryptocurrencies such as Litecoin, Dogecoin, and Ethereum are becoming increasingly popular. Although existing blockchain-based cryptocurrency schemes can ensure reasonable security for transactions, they do not consider any notion of fairness. Fair exchange allows two players to exchange digital "items", such as digital signatures, over insecure networks fairly, so that either each player gets the other's item, or neither player does. Given that blockchain participants typically do not trust each other, enabling fairness in existing cryptocurrencies is an essential but insufficiently explored problem. In this paper, we explore the solution space for enabling the fair exchange of a cryptocurrency payment for a receipt. We identify the timeliness of an exchange as an important property especially when one of the parties involved in the exchange is resource-constrained. We introduce the notion of strong timeliness for a fair exchange protocol and propose two fair payment-for-receipt protocol instantiations that leverage functionality of the blockchain to achieve strong timeliness. We implement both and compare their security and efficiency.

Jens Mathias Bohli, Dan Dobre, Ghassan O. Karame et al.

Location-based services are increasingly used in our daily activities. In current services, users however have to give up their location privacy in order to acquire the service. The literature features a large number of contributions which aim at enhancing user privacy in location-based services. Most of these contributions obfuscate the locations of users using spatial and/or temporal cloaking in order to provide k-anonymity. Although such schemes can indeed strengthen the location privacy of users, they often decrease the service quality and do not necessarily prevent the possible tracking of user movements (i.e., direction, trajectory, velocity). With the rise of Geofencing applications, tracking of movements becomes more evident since, in these settings, the service provider is not only requesting a single location of the user, but requires the movement vectors of users to determine whether the user has entered/exited a Geofence of interest. In this paper, we propose a novel solution, PrivLoc, which enables the privacy-preserving outsourcing of Geofencing and location-based services to the cloud without leaking any meaningful information about the location, trajectory, and velocity of the users. Notably, PrivLoc enables an efficient and privacy-preserving intersection of movement vectors with any polygon of interest, leveraging functionality from existing Geofencing services or spatial databases. We analyze the security and privacy provisions of PrivLoc and we evaluate the performance of our scheme by means of implementation. Our results show that the performance overhead introduced by PrivLoc can be largely tolerated in realistic deployment settings.