Software monocultures are usually considered dangerous because their size and uniformity represent the potential for costly and widespread damage. The emerging concept of collaborative security provides the opportunity to re-examine the utility of software monoculture by exploiting the homogeneity and scale that typically define large software monocultures. Monoculture can be leveraged to improve an application’s overall security and reliability. We introduce and explore the concept of Application Communities: collections of large numbers of independent instances of the same application. Members of an application community share the burden of monitoring for flaws and attacks, and notify the rest of the community when such are detected. Appropriate mitigation mechanisms are then deployed against the newly discovered fault. We explore the concept of an application community and determine its feasibility through analytical modeling and a prototype implementation focusing on software faults and vulnerabilities. Specifically, we identify a set of parameters that define application communities and explore the tradeoffs between the minimal size of an application community, the marginal overhead imposed on each member, and the speed with which new faults are detected and isolated. We demonstrate the feasibility of the scheme using Selective Transactional EMulation (STEM) as both the monitoring and remediation mechanism for low-level software faults, and provide some preliminary experimental results using the Apache web server as the protected application. Our experiments show that ACs are practical and feasible for current applications: an AC of 15,000 members can collaboratively monitor Apache for new faults and immunize all members against them with only a 6 % performance degradation for each member. 1

Abstract — Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme – Cluster-based Private Data Aggregation (CPDA)– leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme – Slice-Mix-AggRegaTe (SMART)– builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme – TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks. I.

Event correlation is a widely-used data processing methodology for a broad variety of applications, and is especially useful in the context of distributed monitoring for software faults and vulnerabilities. However, most existing solutions have typically been focused on “intraorganizational” correlation; organizations typically employ privacy policies that prohibit the exchange of information outside of the organization. At the same time, the promise of “interorganizational” correlation is significant given the broad availability of Internet-scale communications, and its potential role in both software maintenance and software vulnerability exploits. In this proposal, I present a framework for reconciling these opposing forces in event correlation via the use of privacy preservation integrated into the event processing framework. By integrating flexible privacy policies, we enable the correlation of organizations ’ data without actually releasing sensitive information. The framework supports both source anonymity and data privacy, yet allows for the time-based correlation of a broad variety of data. The framework is designed as a lightweight collection of components to enable integration with existing COTS platforms and distributed systems. I also present two different implementations of this framework:

Building on the popularity of online social networks (OSNs) such as Facebook, social content-sharing applications allow users to form communities around shared interests. Millions of users worldwide use them to share recommendations on everything from music and books to resources on the web. However, their increasing popularity is beginning to attract the attention of malicious attackers. As social network credentials become valued targets of phishing attacks and social worms, attackers look to leverage compromised accounts for further financial gain. In this paper, we analyze the state of privacy protection in social content-sharing applications, describe effective privacy attacks against today’s social networks, and propose anonymization techniques to protect users. We show that simple protection mechanisms such as anonymizing shared data can still leave users open to social intersection attacks, where a small number of compromised users can identify the originators of shared content. Modeling this as a graph anonymization problem, we propose to provide users with k-anonymity privacy guarantees by augmenting the social graph with “latent edges. ” We identify StarClique, a locally minimal graph structure required for users to attain k-anonymity, where at worst, a user is identified as one of k possible contributors of a data object. We prove the correctness of our approach using analysis. Finally, using experiments driven by traces from the del.icio.us social bookmark site, we demonstrate the practicality and effectiveness of our approach on real-world systems.

Recent work [27, 15] introduced a novel peer-to-peer application that leverages content sharing and aggregation among the peers to diagnose misconfigurations on a desktop PC. This application poses interesting challenges in preserving privacy of user configuration data and in maintaining integrity of troubleshooting results. In this paper, we provide a much more rigorous cryptographic and yet practical solution for preserving privacy, and we investigate and analyze solutions for ensuring integrity.

This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining tasks in a distributed environment such as clustering, anomaly detection, target tracking to name a few. This technology is crucial for many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Centralizing all or some of the data for building global models is impractical in such peer-to-peer environments because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. The distributed algorithm we have developed in this paper is provably-correct i.e. it converges to the same result compared to a similar centralized algorithm and can automatically adapt to changes to the data and the network. We show that the communication overhead of the algorithm is very low due to its local nature. This monitoring algorithm is then used as a feedback loop to sample data from the network and rebuild the model when it is outdated. We present thorough experimental results to verify our theoretical claims.

National Science Foundation under subcontract no. SA4896-10808PG. The views and conclusions contained herein are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government, or any other governmental, commercial or legal entity.

Abstract — Data aggregation is an efficient mechanism widely used in wireless sensor networks (WSN) to collect statistics about data of interests. However, the shared-medium nature of communication makes the WSNs are vulnerable to eavesdropping and packet tampering/injection by adversaries. Hence, how to protect data privacy and data integrity are two major challenges for data aggregation in wireless sensor networks. In this paper, we present iPDA — an integrity-protecting private data aggregation scheme. In iPDA, data privacy is achieved through data slicing and assembling technique; and data integrity is achieved through redundancy by constructing disjoint aggregation paths/trees to collect data of interests. In iPDA, the data integrity-protection and data privacy-preservation mechanisms work synergistically. We evaluate the performance of iPDA scheme in terms of communication overhead and data aggregation accuracy, comparing with a typical data aggregation scheme – TAG, where no integrity protection and privacy preservation is provided. Simulation results show that iPDA achieves the design goals while still maintains the efficiency of data aggregation. 1 I.

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Abstract. In the context of early warning systems for detecting Internet worms and other attacks, event correlation techniques are needed for two reasons. First, network attack detection is usually based on distributed sensors, e.g. intrusion detection systems. During attacks but even in normal operation, the generated amount of events is hard to handle in order to evaluate the current attack situation for a larger network. Thus, the concept of event or alert correlation has been introduced. This survey was motivated by recent work on early warning systems. We summarize and clarify the typical terminology used in this context and present a requirement analysis from an early warning system’s point of view. In the main part of this survey, we summarize and classify event correlation techniques as described in the literature. 1