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We investigated the practical usefulness of solutions based on secure multiparty computation

SEPIA: privacy-preserving aggregation of multi-domain network events and statistics

USENIX Security Symposium, pp.15-15, (2010)

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Abstract

Secure multiparty computation (MPC) allows joint privacy-preserving computations on data of multiple parties. Although MPC has been studied substantially, building solutions that are practical in terms of computation and communication cost is still a major challenge. In this paper, we investigate the practical usefulness of MPC for multi-...More

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Introduction
  • A number of network security and monitoring problems can substantially benefit if a group of involved organizations aggregates private data to jointly perform a computation.
  • Aggregation of private data is useful for alert signature extraction [30], collaborative anomaly detection [34], multi-domain traffic engineering [27], detecting traffic discrimination [45], and collecting network performance statistics [42]
  • All these approaches use either a trusted third party, e.g., a university research group, or peer-to-peer techniques for data aggregation and face a delicate privacy versus utility tradeoff [32].
  • One possible solution to this privacy-utility tradeoff is MPC
Highlights
  • A number of network security and monitoring problems can substantially benefit if a group of involved organizations aggregates private data to jointly perform a computation
  • Since data volume is dominated by privacy peer messages, we show the average bytes sent per privacy peer in one time window in Fig. 6b
  • The aggregation of network security and monitoring data is crucial for a wide variety of tasks, including collaborative network defense and cross-sectional Internet monitoring
  • We investigated the practical usefulness of solutions based on secure multiparty computation (MPC)
  • We believe that our work provides useful insights into the practical utility of multiparty computation and paves the way for new collaboration initiatives
  • In collaboration with a major systems management vendor, we have started a project that aims at incorporating multiparty computation primitives into a mainstream traffic profiling product
Methods
  • Design and Implementation

    The foundation of the SEPIA library is an implementation of the basic operations, such as multiplications and optimized comparisons, along with a communication layer for establishing SSL connections between input and privacy peers.
  • In order to limit the impact of varying communication latencies and response times, each connection, along with the corresponding computation and communication tasks, is handled by a separate thread.
  • This implies that SEPIA protocols benefit from multi-core systems for computationintensive tasks.
  • This gives the input peers the ability to define an acceptable level of privacy by only participating in the computation if a certain number of other input/privacy peers participate
Conclusion
  • The aggregation of network security and monitoring data is crucial for a wide variety of tasks, including collaborative network defense and cross-sectional Internet monitoring.
  • The authors investigated the practical usefulness of solutions based on secure multiparty computation (MPC)
  • For this purpose, the authors designed optimized MPC operations that run efficiently on voluminous input data.
  • The authors designed optimized MPC operations that run efficiently on voluminous input data
  • The authors implemented these operations in the SEPIA library along with a set of novel protocols for event correlation and for computing multi-domain network statistics, i.e., entropy and distinct count.
  • In collaboration with a major systems management vendor, the authors have started a project that aims at incorporating MPC primitives into a mainstream traffic profiling product
Tables
  • Table1: Comparison of LAN and PlanetLab settings
  • Table2: Comparison of frameworks performance in operations per second with m = 5
  • Table3: Organizations profiting from an early anomaly warning by aggregation
Download tables as Excel
Related work
  • Most related to our work, Roughan and Zhan [37] first proposed the use of MPC techniques for a number of applications relating to traffic measurements, including the estimation of global traffic volume and performance measurements [36]. In addition, the authors identified that MPC techniques can be combined with commonlyused traffic analysis methods and tools, such as timeseries algorithms and sketch data structures. Our work is similar in spirit, yet it extends their work by introducing new MPC protocols for event correlation, entropy, and distinct count computation and by implementing these protocols in a ready-to-use library.

    Data correlation systems that provide strong privacy guarantees for the participants achieve data privacy by means of (partial) data sanitization based on bloom filters [44] or cryptographic functions [26, 24]. However, data sanitization is in general not a lossless process and therefore imposes an unavoidable tradeoff between data privacy and data utility.

    The work presented by Chow et al [12] and Applebaum et al [1] avoid this tradeoff by means of cryptographic data obfuscation. Chow et al proposed a two-party query computation model to perform privacypreserving querying of distributed databases. In addition to the databases, their solution comprises three entities: the randomizer, the computing engine, and the query frontend. Local answers to queries are randomized by each database and the aggregate results are derandomized at the frontend. Applebaum et al present a semi-centralized solution for the collaboration among a large number of participants in which responsibility is divided between a proxy and a central database. In a first step the proxy obliviously blinds the clients’ input, consisting of a set of keyword/value pairs, and stores the blinded keywords along with the non-blinded values in the central database. On request, the database identifies the (blinded) keywords that have values satisfying some evaluation function and forwards the matching rows to the proxy, which then unblinds the respective keywords. Finally, the database publishes its non-blinded data for these keywords. As opposed to these approaches, SEPIA does not depend on two central entities but in general supports an arbitrary number of distributed privacy peers, is provably secure, and more flexible with respect to the functions that can be executed on the input data. The similarities and differences between our work and existing general-purpose MPC frameworks are discussed in Sec. 5.4.
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