A Link Flooding (LFA) attack is a special type of Denial-of-Service (DoS) attack in which the attacker sends out a huge number of requests to exhaust the capacity of a link on the path the traffic comes to a server. As a result, user traffic cannot reach the server. As a result, DoS and degradation of Quality-of-Service (QoS) occur. Because the attack traffic does not go to the victim, protecting the legitimate traffic alone is hard for the victim. The victim can protect its legitimate traffic by using a special type of router called filter router (FR). An FR can receive server filters and apply them to block a link incident to it. An FR probabilistically appends its own IP address to packets it forwards, and the victim uses that information to discover the traffic topology. By analyzing traffic rates and paths, the victim identifies some links that may be congested. The victim needs to select some of these possible congested links (PCLs) and send a filter to the corresponding FR so that legitimate traffic avoids congested paths. In this paper, we formulate two optimization problems for blocking the least number of PCLs so that the legitimate traffic goes through a non-congested path. We consider the scenario where every user has at least one non-congested shortest path in the first problem. We extend the first problem to a scenario where there are some users whose shortest paths are all congested. We transform the original problem to the vertex separation problem to find the links to block. We use a custom-built Java multi-threaded simulator and conduct extensive simulations to support our solutions.

Summary In recent times, many location‐based service providers (LBSPs) choose to outsource data query services to third‐party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in‐vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead.

A transit-link distributed denial-of-service (DDoS) attack is a special attack in which the attacker sends out a huge number of requests to exhaust the capacity of a link on the path the traffic comes to a server. As a result, denial-of-service and degradation of Quality-of-Service (QoS) occurs. Because the attack traffic does not go to the victim, protecting the legitimate traffic alone is hard for the victim. With the help of a special type of router called filter router (FR), the victim can protect the legitimate traffic. A FR can receive filter from servers and apply the filter to block a link incident to it. By analyzing traffic rates and paths, the victim can identify some links that may be congested. The victim needs to select some of these possible congested links and send a filter to the corresponding FR so that the legitimate traffic follows non-congested paths. In this paper, we formulate an optimization problem for selecting the minimum number of possible congested links so that the legitimate traffic goes through a non-congested path. We consider the scenario where every user has at least one non-congested shortest path. We transform the problem to the vertex separation problem to find the links to block. We build our own Java multi-threaded simulator and conduct extensive simulations.

Consider a database where each record has multiple attributes. An untrusted server is in charge of processing queries over this database, and we want to provide a mechanism for users to verify the correctness of their query results. Here each query, referred to as a multi-dimensional top-k query, retrieves k records whose output with user-supplied ranking function is among top k. Multi-dimensional top-k query is widely used in real applications. However, as the traditional query authentication methods cannot be directly deployed on multi-dimensional top-k query, it is still a challenging problem to authenticate the multi-dimensional top-k query results. In this paper, we propose an authentication solution to support multi-dimensional top-k query based on signature chain. By using signature chain for each record and its successors on each dimension, our solution allows users to efficiently verify the soundness and completeness of multi-dimensional top-k query results. Through theoretical analysis and simulation, we demonstrate the effectiveness of our proposed solution.

In the smart cities of the future, people expect to gather data from moving vehicles. Due to the existence of malicious users who claim to be in a traffic flow but actually are in others, a location proof for vehicular trajectory-based data needs to be developed. RoadSide Units (RSUs) are commonly used in smart cities, and a vehicular trajectory's location proof can be generated based on messages collected from RSUs along the trajectory. This paper studies the optimal RSU placement problem: Given a set of traffic flows, the objective is to place a minimum number of RSUs to securely distinguish all flows. A traffic flow is securely distinguishable if the set of its passing RSUs is unique among all traffic flows and unforgeable. To solve this problem, an RSU placement algorithm with an approximation ratio O(ln n) is proposed. In order to further reduce the number of deployed RSUs, this paper explores the credential propagation mechanism via Car-to-Car (C2C) communications, which essentially extend the coverage of an RSU. Approximation algorithms are proposed to solve the problem, and extensive real data-driven experiments demonstrate the efficiency and effectiveness of the proposed algorithms.

As a shared economy platform, Airbnb provides collaborative practices for customers and guides them to match with hosts' rooms. Based on the records and ratings, there is great significance attached to inferring the satisfaction between users and rooms. Several essential problems arise when evaluating satisfaction and matching. Data confidence and prediction bias influence the inference performance of the satisfaction. When two users stay in a room, the two users' joint satisfaction also deserves particular research because of the roommate effect. In this paper, the satisfaction is inferred considering confidence and prediction uncertainties. The satisfaction with the confidence uncertainty is modeled using a normalized variance of the Beta distribution. The algorithms for inferring satisfaction with the prediction uncertainties are divided into two parts: a weighted matrix factorization-based algorithm for individuals and a preference similarity-based algorithm for pairs. The problem can be reduced to a matching problem. Finally, extensive experiments show the effectiveness and accuracy of the proposed method.

Data is becoming more and more valuable as technology advances. Through crowdsourcing organizations are able to collect large amounts of data at an effective rate with little cost. This paper proposes a crowdsourcing-based music playing system, where the next song to play is determined by the listening preferences of realtime online users. Unlike conventional radio play system, where songs are randomly selected, our system selects songs which satisfies the listening preferences of a majority of users, who are currently online. Our system consists of two important components: a predictor, which estimates a user's listening preferences based on his features, and a decision maker, which selects the next song to play based on a majority vote. In order to find out the relationship between user features and listening preferences, K-means algorithm is adopted. A large amount of real data is collected via online surveys, and extension simulations show that our system can effectively selects a proper song for its online audience.

Traffic flow monitoring systems aim to measure and monitor vehicle trajectories in smart cities. Their critical applications include vehicle theft prevention, vehicle localization, and traffic congestion solution. This paper studies an RoadSide Unit (RSU) placement problem in traffic flow monitoring systems. Given some traffic flows on streets, the objective is to place a minimum number of RSUs to cover and distinguish all traffic flows. A traffic flow is covered and distinguishable, if the set of its passing RSUs is non-empty and unique among all traffic flows. The RSU placement problem is NP-hard, monotonic, and non-submodular. It is a non-trivial extension of the traditional set cover problem that is submodular. We show that, to cover and distinguish an arbitrary pair of traffic flows (ƒ and ƒ′), two RSUs should be placed on streets from two different subsets of ƒ∖ƒ′, ƒ′∖ƒ, and ƒ ⋂ ƒ′. Three bounded RSU placement algorithms are proposed. Their approximation ratios are n ln n(n−1)/2, n+1/2 ln 3n(n−1)/2, and ln n(n+1)/2, respectively. Here, n is the number of given traffic flows. Extensive real data-driven experiments demonstrate the efficiency and effectiveness of the proposed algorithms.

Social crowdsourcing is a special outsourcing plat- form, on which a complex job is outsourced onto a social network of workers. By interactively recruiting friends of friends, and assigning small pieces of works to them, eventually a large job can be completed. However, due to the workflows presenting a tree-like structure, the reliability of the current social crowdsourcing system is poor. The absence of a relay worker will cause disconnection between the job owner and sub-workers. For increasing the reliability, we explore two special social structures. One is based on a triangle relation among any three consecutive workers on the workflows; the other one is based on a quadrilateral relation between two physically intersected workflows. Based on these structures, we propose several social crowdsourcing return rules. Theoretical analysis and simulation results show that our proposed schemes can significantly improve the performance and reliability of social crowdsourcing.

Video-on-demand (VoD) service has been explosively growing since its first appearance. For maintaining an acceptable buffering delay, the bandwidth costs have become a huge burden for the service providers. Complementing the conventional client-server architecture with a peer-to-peer system(P2P) can significantly reduce the central server's bandwidth demands. However, the previous works focus on establishing a P2P overlay for each video, producing a high maintenance cost on users. Per-channel-based overlay construction was first introduced by Social Tube, which clusters the users subscribed to the same video channels into one P2P overlay. However, the current per-channel overlay structure is not suitable for users developing new watching preferences. Consider that a channel's subscribers tend to watch not only the videos from the channel, but also other videos from similar channels. In this paper, we propose a new overlay structure by exploring the existing social relations of users and the similarities of video channels. Our system creates a hierarchical overlay: subscribers of the same channel form the low-level overlay (also known as groups), and in high-level overlay, different groups are connected based on their similarities. The new structure has the small-world property, the existence of which has been found in most data-sharing patterns. Based on the new structure, we propose a routing algorithm for both channel subscribed and unsubscribed users. Extensive simulation results show the efficiency of our approach.

There are plenty of idle computational resources on the Internet, which could potentially be used for accomplishing huge tasks. More and more applications are being designed for exploring those idle resources. In this paper, we focus on the idle computational resources, including both human intelligence and machine computing abilities, in mobile social networks (MSNs). Based on the unique features of MSN, we design a new cooperative system, called social-crowdsourcing. The distributed and infrastructure-free features of the system make it more attractive than traditional crowdsourcing platforms. In the proposed system, a huge work is gradually partitioned into smaller pieces, and is propagated from node to node. However, how to partition and allocate these segments is a critical problem, which directly affects the work's completion time and system throughput. Due to the lack of global information, independent relay nodes are likely to make conflicted decisions, which will cause an unbalanced workload distribution on participating nodes. In this paper, we find that, for a work at different processing stages, one should adopt distinct workload exchanging schemes, moving from a progressive method to a conservative one. Based on this observation, we propose an adaptive workload allocation scheme, in which a participating node can gradually switch his decision strategy according to the workload statuses of neighboring nodes. By using our approach, system throughput can be significantly improved, and large works can finish within a nearly optimal time. Unlike in traditional scheduling problems, we take a human's rejection, contact delay, and social similarity into consideration. Extensive simulation results show that our proposed algorithms can successfully make full use of the idle resources in MSNs.

Mobile social networks are vulnerable to Sybil attacks. By creating a large number of fake identities, malicious users can gain a disproportionately high benefit through a Byzantine fashion. Most social network-based Sybil defenses adopt the assumptions that the honest region is a fast-mixing network. However, more and more evidence shows that some real social networks are not fast-mixing, especially when only strong-trust relations are considered. Moreover, the accuracy of all existing solutions is related to the number of attack edges that the adversary can build. In this paper, for addressing these problems, we propose a local ranking system for estimating trust-level between users. Our scheme has three unique features. First, our system is based on both trust and distrust relations. Second, instead of storing the entire social graph, users carry limited information related to themselves. Last but not least, our system weakens the impacts of attack edges by removing several suspicious edges with high centrality. We validate the effectiveness of our solutions through comprehensive experiments.

Cooperative trajectory mapping is an emerging technique that allows users to create a map by using data collected from each participant's mobile phones. Unlike the traditional localization problem, where GPS is usually required, cooperative mapping only requires information about the relative distance and direction from the previously reported position. In this paper, we consider the problem of measurement error, which is when the measurement error causes the spatial relations among users to be wrong, in cooperative trajectory mapping. We propose an encounter-based error canceling algorithm to efficiently reduce measurement errors. Extensive simulation experiments are performed to validate our solutions.

Map construction is an integral part of many location-based services. In this paper, we propose a feedback-based heuristic map construction algorithm (FHMCA). This is a lightweight, cooperative, map construction technique which can accurately capture the unique characteristics of each intersection and the length of every road without requiring the users to transmit large amounts of data or use GPS. The proposed algorithm improves the bandwidth and energy efficiency of cooperative map construction as no detailed maps are needed. However, the resulting map is still useful for location-based services. Moreover, our method is applicable when the existing of malicious users, who report wrong data. We validate the effectiveness of our solutions through extensive simulation experiments.

Privacy is an important concern for location based services (LBSs). In this paper, we consider a specific type of LBS known as a mobile social network (MSN). We demonstrate a new type of attack, where an adversary can combine the location and friendship information found in a MSN, to violate user privacy. We propose a fake location reporting solution that does not require any additional trusted third party deployment. We use extensive simulations to determine the validity of our scheme.