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Recent years have witnessed the proliferation of mobile crowd sensing (MCS) systems that leverage the public crowd equipped with various mobile devices (e.g., smartphones, smartglasses, smartwatches) for large scale sensing tasks. Because of the importance of incentivizing worker participation in such MCS systems, several auction-based incentive mechanisms have been proposed in past literature. However, these mechanisms fail to consider the preservation of workers' bid privacy. Therefore, different from prior work, we propose a differentially private incentive mechanism that preserves the privacy of each worker's bid against the other honest-but-curious workers. The motivation of this design comes from the concern that a worker's bid usually contains her private information that should not be disclosed. We design our incentive mechanism based on the single-minded reverse combinatorial auction. Specifically, we design a differentially private, approximately truthful, individual rational, and computationally efficient mechanism that approximately minimizes the platform's total payment with a guaranteed approximation ratio. The advantageous properties of the proposed mechanism are justified through not only rigorous theoretical analysis but also extensive simulations.

Distributed Hash Table (DHT) lookup is a core technique in structured peer-to-peer (P2P) networks. Its decentralized nature introduces security and privacy vulnerabilities for applications built on top of them; we thus set out to design a lookup mechanism achieving both security and anonymity, heretofore an open problem. We present Octopus, a novel DHT lookup which provides strong guarantees for both security and anonymity. Octopus uses attacker identification mechanisms to discover and remove malicious nodes, severely limiting an adversary's ability to carry out active attacks, and splits lookup queries over separate anonymous paths and introduces dummy queries to achieve high levels of anonymity. We analyze the security of Octopus by developing an event-based simulator to show that the attacker discovery mechanisms can rapidly identify malicious nodes with low error rate. We calculate the anonymity of Octopus using probabilistic modeling and show that Octopus can achieve near-optimal anonymity. We evaluate Octopus's efficiency on Planetlab with 207 nodes and show that Octopus has reasonable lookup latency and manageable communication overhead.

Network intruders try to hide their identity by relaying their traffic through a number of intermediate hosts, called stepping stones. Network flow watermarks have been used to detect such attacks by inserting a special timing pattern into one flow by means of artificial delays and detecting relayed flows by searching for the same pattern. We study the application of coding schemes to improve the efficiency of network flow watermarks. In particular, we use the Repeat-Accumulate codes, a class of low complexity, high performance error-correcting codes, to improve the detection performance of a recent flow watermark, the RAINBOW. We show the effectiveness of the improved scheme, C-RAINBOW, through simulation and discuss design tradeoffs.

This paper presents a dangerous low-cost traffic analysis attack in packet-based networks, such as the Internet. The attack is mountable in any scenario where a shared routing resource exists among users. A real-world attack successfully compromised the privacy of a user without requiring significant resources in terms of access, memory, or computational power. The effectiveness of our attack is demonstrated in a scenario where the user's DSL router uses FCFS scheduling policy. Specifically, we show that by using a low-rate sequence of probes, a remote attacker can obtain significant traffic-timing and volume information about a particular user, just by observing the round trip time of the probes. We also observe that even when the scheduling policy is changed to round-robin, while the correlation reduces significantly, the attacker can still reliably deduce user's traffic pattern. Most of the router scheduling policies designed to date are evaluated mostly on the metrics of throughput, delay and fairness. Our work is aimed to demonstrate a need for considering an additional metric that quantifies the information leak between the individual traffic flows through the router.

In this work we present a Multi-flow Attack Resistant Interval Centroid Based Watermarking (MAR-ICBW) scheme for network flows. Our proposed scheme can withstand the newly introduced multi-flow watermarking attack that defeats the state-of-the-art interval-based network flow watermarking schemes. Multi-flow attack uses the dependent correlations among the flows marked with the same watermark to recover the secret parameters, and remove the watermark from a flow. The attack can be effective even if different flows are marked with different values of a watermark. MAR-ICBW survives the attack by virtue of randomizing the location of the embedded watermark across multiple flows and therefore, effectively removing the correlations between the flows. While we represent our counter measure to multi-flow attack in terms of an improved version of ICBW, the same methodology can be used to strengthen other interval-based flow watermarking schemes.

We consider the problem of defending against Sybil attacks using computational puzzles. A fundamental difficulty in such defenses is enforcing that puzzle solutions not be reused by attackers over time. We propose a fully decentralized scheme to enforce this by continually distributing locally generated challenges that are then incorporated into the puzzle solutions. Our approach consists of an all-to-all broadcast of challenges, with a combining function to ensure this can be done efficiently. The combining function generates certificates that can be used to prove that each node's challenge was delivered to and used by each other node, therefore proving the freshness of each puzzle. We show how our distribution and verification mechanisms can be implemented on top of the the Chord in Stoica et al., (2001) overlay

As security monitoring grows both more complicated and more sophisticated, there is an increased demand for outsourcing these tasks to Managed Security Service Providers (MSSPs). However, the core problem of sharing private security logs creates a barrier to the widespread adoption of this business model. In this paper we analyze the logs used for security analysis with the concern of privacy and propose the constraints on anonymization of security monitor logs. We believe if the anonymization solution fulfills the constraints, MSSPs can detect the attacks efficiently and protect privacy simultaneously.