The increasing amount of graph-structured data catalyzes analytics over graph databases using semantic queries. Motivated by the ubiquity of commercial cloud platforms, data owners are willing to store their graph databases remotely. However, data privacy has emerged as a widespread concern since the cloud platforms are not fully trusted. One viable solution is to encrypt sensitive data before outsourcing, which inevitably hinders data retrieval. To enable queries over encrypted data, searchable symmetric encryption (SSE) has been introduced. Yet, the most well-studied class of SSE schemes focuses on retrieving textual files given keywords, which cannot be applied to graph databases directly. This paper extends our preliminary work (FC′17) and proposes GraphShield, a structured encryption scheme for graphs. Beyond shortest distance queries, GraphShield can support other classic graph-based queries (e.g., maximum flow) and more complicated analytics (e.g., PageRank). Technically, we incorporate a suite of (efficient) cryptographic primitives and tailor some extra secure protocols for facilitating graph analytics. Our scheme also allows updates on the encrypted graph with forward privacy guaranteed. We formalize the security model and prove the adaptive security with reasonable leakage. Finally, we implement our scheme on various real-world datasets, and the experiment results demonstrate its practicality and scalability.

Mining pools have become dominant in today's bitcoin mining network, where miners can pool their powers together for reduced variance of block mining and steadier stream of potential income. Along with the continuous evolvement of mining pools are the increasingly intense competitions among them. Recent empirical studies have shown that the distributed denial-of-service (DDoS) attack is one of the most common ways for competing mining pools to sabotage the rivals and earn illegitimate rewards. Existing efforts have been made on using static game models to analyze the interactions between mining pools, and derive the Nash Equilibrium and optimal attacking strategies in a one-time static context. To better understand the impact of such DDoS attacks, in this paper, we take a starkly different approach, and for the first time address the dynamics in mining pool attacks. Specifically, we start by formulating the interactive competition among mining pools as a general-sum stochastic game. Then we propose an efficient Nash learning algorithm to obtain the near optimal attacking strategy that maximizes the expected long-term utility. Our theoretical analysis and extensive experimental results both show that the proposed strategy outperforms the baseline myopic learning algorithm, which only aims at maximizing the revenue in the current time stage. These findings, together with our proposed stochastic game model and learning algorithm, are expected to provide more practical guidelines for mining pools to survive and thrive in the highly-competitive bitcoin ecosystem.

Cuckoo search algorithm (CS), as a new heuristic algorithm, has been paid increasing attention and studied by many scholars because of its efficient performance. However, premature convergence is a defect of CS. Recently, some heuristic algorithms have been successfully applied to some high performance computing frameworks, effectively overcome the premature convergence problem of the algorithm, which provides us with a new idea to enhance CS. Therefore, a novel CS variant, called Spark-based gaussian bare-bones cuckoo search with dynamic parameter selection (SparkGDCS), which combines a novel CS variant with the efficient Spark framework, is proposed in this paper. In SparkGDCS, GDCS is a new variant which combines Gaussian bare-bones strategy and dynamic parameter selection, whose purpose is to enhance the search ability of CS. Finally, by testing the benchmark functions proposed by the 2010 and 2013 IEEE Congress on Evolutionary Computation special session (CEC 2010 and CEC 2013), comprehensive experiments prove the effectiveness of SparkGDCS.

Auction is an effective way to allocate goods or services to bidders who value them the most. The rapid growth of e-auctions facilitates online transactions but poses new and distinctive challenges. It is difficult to establish trust among sellers, buyers, and auctioneers without centralized auction websites or platforms (the auctioneer) which collect bids and derive the auction results. However, these third parties may be untrustworthy, and malicious sellers or buyers may refuse to deliver the goods or payment according to the protocol. Moreover, the open and anonymous online environment may stimulate auction participants to form collusion coalitions to rig the auction and reap unfair profit. Many auction designs have been proposed to address these concerns, but they fall short of simultaneously achieving decentralization (i.e., held without a trusted third utility), strong consensus (i.e., the establishment of trust), collusion resistance, and practical implementation. We present CReam, the first decentralized collusion-resistant e-auction system that is implemented with smart contract on the blockchain. With the carefully designed smart auction contract, mutually distrustful and rational sellers and buyers are stimulated to operate properly, hence transact safely without trusted third parties. The auction mechanism in the smart contract can effectively prevent bidder collusion and realize economic robustness, i.e., truthfulness. We implement a fully functional CReam on the Ethereum network. Extensive experimental results confirm that CReam can greatly reduce the probability of collusion and achieve an approximate optimal revenue at a low cost of contract execution.