Query privacy has led to a serious concern for location-based services (LBSs). Existing mainstream schemes preserve query privacy by either introducing a third party or degrading the accuracy of LBS. To address these deficiencies, we propose a scheme named the content sealed bottle in this paper for the users to get accurate LBSs while the server learns zero knowledge on the query content.To realize this objective, the Euclidean distance based on homomorphic encryption is used to signal the difference between the query attribute vector of a user and each database attribute vector of the server; then a boolean circuit is designed to identify the exact match for the query attribute vector. Through optimizing the traditional sub-circuits, the total number of non-XOR gates, which is the main cost during the matching process, can be reduced by a factor of l compared to that of the traditional M-to-1 MIN sub-circuit, where l is the input bit length of the boolean circuit. Finally, the computationally private information retrieval technique is employed to privately retrieve the POI set. To the best of our knowledge, our content sealed bottle is the first scheme that can achieve cost-effective query privacy preservation without the involvement of a third party or sacrificing the accuracy of LBSs. Our security analysis and extensive experimental results demonstrate the effectiveness of the proposed scheme.

The integration of directed acyclic graph (DAG)-based blockchain and Internet of Things (IoT) aims at improving the efficiency of data storage. However, if massive IoT data are not placed in an organized way, the search and usage of the data for upper-level applications can be burdensome, since they have to examine the data block by block, which also increases the difficulty of data verification, affecting consensus efficiency. To maintain the high throughput advantage of DAG-based blockchain applied in IoT and improve the data analysis efficiency, we propose a novel consensus mechanism named TidyBlock, including the transaction collation mechanism for block generation and the block selection mechanism for verification. The first mechanism can tidy up scattered transactions before they are packaged into blocks, while the second one can collate blocks to facilitate verification, realizing a two-layer collation of IoT data so as to increase analysis efficiency of upper-level IoT applications. Additionally, the second mechanism can provide a self-driven incentive for rational participants to follow the first one in case they are reluctant to do extra collation work. Theoretical analysis is provided to demonstrate the validity of our proposed algorithms by formal methods. Extensive simulations based on synthetic data verify the rationality and effectiveness of the proposed mechanisms.

Pocket switched networks (PSNs) take advantage of human mobility to deliver data. Investigations on real-world trace data indicate that human mobility shows an obvious spatial regularity: a human being usually visits a few places at high frequencies. These most frequently visited places form the home of a node, which is exploited in this paper to design two HomE based Relay selectiOn (HERO) algorithms. Both algorithms input single data copy into the network at any time. In the basic HERO, only the first node encountered by the source and whose home overlaps a destination’s home is selected as a relay while the enhanced HERO keeps finding more optimal relay that visits the destination’s home with higher probability. The two proposed algorithms only require the relays to exchange the information of their home and/or the visiting frequencies to their home when two nodes meet. As a result, the information update is reduced and there is no global status information that needs to be maintained. This causes light loads on relays because of the low communication cost and storage requirements. Additionally, only simple operations are needed in the two proposed algorithms, resulting in little computation overhead at relays. At last, a theoretical analysis is performed on some key metrics and then the real-world based simulations indicate that the two HERO algorithms are efficient and effective through employing only one or a few relays.

Game theory is an effective analytical tool for crowdsourcing. Existing studies based on it share a commonality: the influence of players’ decisions is bilateral. However, the status is broken by the zero-determinant (ZD) strategy, where the ZD player can unilaterally control the opponent's expected payoff. Thereby, crowdsourcing games trigger conclusions that differ from traditional ones. By addressing three questions, this paper is the first work to analyze the turbulence in crowdsourcing caused by the inequality between the requestor and the worker in the ZD game. The first question reveals the potential for the requestor to exploit the worker; the second question quantifies the worker's tolerance towards exploitation, providing a basis for confrontation; the third question serves as the cornerstone for maintaining the crowdsourcing, regulating the requestor's exploitative behavior. To answer these questions, we extend ZD strategies from binary games to continuous ones, not only revealing the requestor's dominance but also enriching the theoretical system of ZD strategies and broadening their application. Furthermore, we introduce the worker's dissatisfaction degree, identifying the exponential trend and decay rate, revealing optimal timing and speed for the worker's effective confrontation and maximum exploitation for the requestor. Numerical simulations have validated the effectiveness of our analyses.