Social Network Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence

Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

/pdf/the-knowledge-complexity-of-interactive-proof-systems-31apre0ecf.pdf

The knowledge complexity of interactive proof-systems

Reputation systems provide mechanisms to produce a metric encapsulating reputation for a given domain for each identity within the system. These systems seek to generate an accurate assessment in the face of various factors including but not limited to unprecedented community size and potentially adversarial environments.We focus on attacks and defense mechanisms in reputation systems. We present an analysis framework that allows for the general decomposition of existing reputation systems. We classify attacks against reputation systems by identifying which system components and design choices are the targets of attacks. We survey defense mechanisms employed by existing reputation systems. Finally, we analyze several landmark systems in the peer-to-peer domain, characterizing their individual strengths and weaknesses. Our work contributes to understanding (1) which design components of reputation systems are most vulnerable, (2) what are the most appropriate defense mechanisms and (3) how these defense mechanisms can be integrated into existing or future reputation systems to make them resilient to attacks.

/pdf/a-survey-of-attack-and-defense-techniques-for-reputation-p5nrx4omw1.pdf

A survey of attack and defense techniques for reputation systems

Software fault localization, the act of identifying the locations of faults in a program, is widely recognized to be one of the most tedious, time consuming, and expensive – yet equally critical – activities in program debugging. Due to the increasing scale and complexity of software today, manually locating faults when failures occur is rapidly becoming infeasible, and consequently, there is a strong demand for techniques that can guide software developers to the locations of faults in a program with minimal human intervention. This demand in turn has fueled the proposal and development of a broad spectrum of fault localization techniques, each of which aims to streamline the fault localization process and make it more effective by attacking the problem in a unique way. In this article, we catalog and provide a comprehensive overview of such techniques and discuss key issues and concerns that are pertinent to software fault localization as a whole.

A Survey on Software Fault Localization

In disruption-tolerant networks (DTNs), network topology constantly changes and end-to-end paths can hardly be sustained. However, social network properties are observed in many DTNs and tend to be stable over time. To utilize the social network properties to facilitate packet forwarding, we present LocalCom, a community-based epidemic forwarding scheme that efficiently detects the community structure using limited local information and improves the forwarding efficiency based on the community structure. We define similarity metrics according to nodes' encounter history to depict the neighboring relationship between each pair of nodes. A distributed algorithm, which only utilizes local information, is then applied to detect communities and the formed communities have strong intra-community connections. We also present two schemes to first select and then prune gateways that connect communities to control redundancy and facilitate efficient intercommunity packet forwarding. Extensive real-trace-driven simulation results are presented to support the effectiveness of our scheme.

/pdf/localcom-a-community-based-epidemic-forwarding-scheme-in-34s80tfkt9.pdf

LocalCom: A Community-based Epidemic Forwarding Scheme in Disruption-tolerant Networks

Nodes in disruption-tolerant networks (DTNs) usually exhibit repetitive motions. Several recently proposed DTN routing algorithms have utilized the DTNs' cyclic properties for predicting future forwarding. The prediction is based on metrics abstracted from nodes' contact history. However, the robustness of the encounter prediction becomes vital for DTN routing since malicious nodes can provide forged metrics or follow sophisticated mobility patterns to attract packets and gain a significant advantage in encounter prediction. In this paper, we examine the impact of the blackhole attack and its variations in DTN routing. We introduce the concept of encounter tickets to secure the evidence of each contact. In our scheme, nodes adopt a unique way of interpreting the contact history by making observations based on the collected encounter tickets. Then, following the Dempster-Shafer theory, nodes form trust and confidence opinions towards the competency of each encountered forwarding node. Extensive real-trace-driven simulation results are presented to support the effectiveness of our system.

/pdf/thwarting-blackhole-attacks-in-disruption-tolerant-networks-1qavz9wg1p.pdf

Thwarting Blackhole Attacks in Disruption-Tolerant Networks using Encounter Tickets

The increasing popularity of social networks has inspired recent research to explore social graphs for marketing and data mining. As social networks often contain sensitive information about individuals, preserving privacy when publishing social graphs becomes an important issue. In this paper, we consider the identity disclosure problem in releasing weighted social graphs. We identify weighted 1*-neighborhood attacks, which assume that an attacker has knowledge about not only a target's one-hop neighbors and connections between them (1-neighborhood graph), but also related node degrees and edge weights. With this information, an attacker may re-identify a target with high confidence, even if any node's 1-neighborhood graph is isomorphic with $k-1$ other nodes’ graphs. To counter this attack while preserving high utility of the published graph, we define a key privacy property, probabilistic indistinguishability, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to anonymize a weighted social graph with such a property. Extensive experiments on both real and synthetic data sets illustrate the effectiveness and efficiency of the proposed scheme.

Preserving Privacy with Probabilistic Indistinguishability in Weighted Social Networks

Binary code similarity detection (BCSD) has many applications, including patch analysis, plagiarism detection, malware detection, and vulnerability search etc. Existing solutions usually perform comparisons over specific syntactic features extracted from binary code, based on expert knowledge. They have either high performance overheads or low detection accuracy. Moreover, few solutions are suitable for detecting similarities between cross-version binaries, which may not only diverge in syntactic structures but also diverge slightly in semantics. In this paper, we propose a solution $\alpha$ Diff, employing three semantic features, to address the cross-version BCSD challenge. It first extracts the intra-function feature of each binary function using a deep neural network (DNN). The DNN works directly on raw bytes of each function, rather than features (e.g., syntactic structures) provided by experts. $\alpha$ Diff further analyzes the function call graph of each binary, which are relatively stable in cross-version binaries, and extracts the inter-function and inter-module features. Then, a distance is computed based on these three features and used for BCSD. We have implemented a prototype of $\alpha$ Diff, and evaluated it on a dataset with about 2.5 million samples. The result shows that $\alpha$ Diff outperforms state-of-the-art static solutions by over 10 percentages on average in different BCSD settings.

$\alpha$ Diff: Cross-Version Binary Code Similarity Detection with DNN

Content-based service, which dynamically routes and delivers events from sources to interested users, is extremely important to network services. However, existing content-based protocols for static networks will incur unaffordable maintenance costs if they are applied directly to the highly mobile environment that is featured in disruption-tolerant networks (DTNs). In this paper, we propose a unique publish/subscribe scheme that utilizes the long-term social network properties, which are observed in many DTNs, to facilitate content-based services in DTNs. We distributively construct communities based on the neighboring relationships from nodes' encounter histories. Brokers are deployed to bridge the communities, and they adopt a locally prioritized pub/sub scheme which combines the structural importance with subscription interests, to decide what events they should collect, store, and propagate. Different trade-offs for content-based service can be achieved by tuning the closeness threshold in community formation or by adjusting the broker-to-broker communication scheme. Extensive real-trace and synthetic-trace driven simulation results are presented to support the effectiveness of our scheme.

Feng Li

Papers

LocalCom: A Community-based Epidemic Forwarding Scheme in Disruption-tolerant Networks

Thwarting Blackhole Attacks in Disruption-Tolerant Networks using Encounter Tickets

Preserving Privacy with Probabilistic Indistinguishability in Weighted Social Networks

$\alpha$ Diff: Cross-Version Binary Code Similarity Detection with DNN

MOPS: Providing Content-Based Service in Disruption-Tolerant Networks