An Efficient Toolkit for Computing Private Set Operations

Private set operation (PSO) protocols provide a natural way of securely performing operations on data sets, such that crucial details of the input sets are not revealed. Such protocols have an ever-increasing number of practical applications, particularly when implementing privacy-preserving data mining schemes. Protocols for computing private set operations have been prevalent in multi-party computation literature over the past decade, and in the case of private set intersection (PSI), have become practically feasible to run in real applications. In contrast, other set operations such as union have received less attention from the research community, and the few existing designs are often limited in their feasibility. In this work we aim to fill this gap, and present a new technique using Bloom filter data structures and additive homomorphic encryption to develop the first private set union protocol with both linear computation and communication complexities. Moreover, we show how to adapt this protocol to give novel ways of computing PSI and private set intersection/union cardinality with only minor changes to the protocol computation. Our work resembles therefore a toolkit for scalable private set computation with linear complexities, and we provide a thorough experimental analysis that shows that the online phase of our designs is practical up to large set sizes

Privacy-Friendly Collaboration for Cyber Threat Mitigation

Sharing of security data across organizational boundaries has often been advocated as a promising way to enhance cyber threat mitigation. However, collaborative security faces a number of important challenges, including privacy, trust, and liability concerns with the potential disclosure of sensitive data. In this paper, we focus on data sharing for predictive blacklisting, i.e., forecasting attack sources based on past attack information. We propose a novel privacy-enhanced data sharing approach in which organizations estimate collaboration benefits without disclosing their datasets, organize into coalitions of allied organizations, and securely share data within these coalitions. We study how different partner selection strategies affect prediction accuracy by experimenting on a real-world dataset of 2 billion IP addresses and observe up to a 105% prediction improvement.Comment: This paper has been withdrawn as it has been superseded by arXiv:1502.0533

Controlled Data Sharing for Collaborative Predictive Blacklisting

Although sharing data across organizations is often advocated as a promising way to enhance cybersecurity, collaborative initiatives are rarely put into practice owing to confidentiality, trust, and liability challenges. In this paper, we investigate whether collaborative threat mitigation can be realized via a controlled data sharing approach, whereby organizations make informed decisions as to whether or not, and how much, to share. Using appropriate cryptographic tools, entities can estimate the benefits of collaboration and agree on what to share in a privacy-preserving way, without having to disclose their datasets. We focus on collaborative predictive blacklisting, i.e., forecasting attack sources based on one's logs and those contributed by other organizations. We study the impact of different sharing strategies by experimenting on a real-world dataset of two billion suspicious IP addresses collected from Dshield over two months. We find that controlled data sharing yields up to 105% accuracy improvement on average, while also reducing the false positive rate.Comment: A preliminary version of this paper appears in DIMVA 2015. This is the full version. arXiv admin note: substantial text overlap with arXiv:1403.212

Approximate Two-Party Privacy-Preserving String Matching with Linear Complexity

Consider two parties who want to compare their strings, e.g., genomes, but do not want to reveal them to each other. We present a system for privacy-preserving matching of strings, which differs from existing systems by providing a deterministic approximation instead of an exact distance. It is efficient (linear complexity), non-interactive and does not involve a third party which makes it particularly suitable for cloud computing. We extend our protocol, such that it mitigates iterated differential attacks proposed by Goodrich. Further an implementation of the system is evaluated and compared against current privacy-preserving string matching algorithms.Comment: 6 pages, 4 figure