SEPIA: Privacy-Preserving Aggregation of Multi-Domain Network Events and Statistics

By Martin Burkhart, Mario Strasser, Dilip Many, Xenofontas A. Dimitropoulos. 2010

365 citations recorded by the source corpus.

Subfields: ML Systems & Hardware, Privacy & Security

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “There is a rich literature exploring secure aggregation in both the single-server setting (via pairwise additive masking [12, 188, 73], via threshold homomorphic encryption [356, 193, 92], and via generic secure multi-party computation [86]) as well as in the multiple non-colluding servers setting [”
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning (2020, background): “A notable recent work is [21], which focuses on optimizing MPC protocols for network security and monitoring.”
Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “, exact max), and some special-case MPC protocols [5, 29, 96] are practical today for certain applications.”
A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, methodology): “We extended SEPIA and implemented other SMC schemes and differential privacy mechanisms.”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “SEPIA [21] is a library for privacy-preserving aggregation of data for network security and monitoring.”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, background): “A few examples include privacy-preserving network security monitoring (Burkhart et al., 2010), privacy-preserving genomics (Wang et al., 2015a; Jagadeesh et al., 2017), private stable matching (Doerner et al., 2016), contact discovery (Li et al., 2013; De Cristofaro et al., 2013), ad conversion…”
Efficient Garbling from a Fixed-Key Blockcipher (2013, background): “Beginning with Fairplay [32], a bit of a cottage industry has emerged to improve the efficiency and practicality of GC-based MPC [2, 7, 9, 10, 12, 17, 22– 24, 28, 29, 31, 36, 37].”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, methodology): “It was followed shortly by VIFF [63], [47] and SEPIA [28], which used the same basic architecture: they took programs written in fairly highlevel languages, converted them to a circuit format, and executed the circuit using a secure computation protocol.”
PrivEx: Private Collection of Traffic Statistics for Anonymous Communication Networks (2014, background): “A closely related work is SEPIA [6] by Burkhart et al. where networks collect data and wish to learn aggregate information about their networks without revealing their individual inputs.”
Secure multiparty aggregation with differential privacy: a comparative study (2013, methodology): “SEPIA is an example framework that implements secure arithmetic operations on shares generated by the Shamir scheme [7].”
Privacy and accountability for location-based aggregate statistics (2011, neutral): “Furthermore, input peers are trusted, so SEPIA does not provide any accountability checks against abusive clients.”
Fast Private Set Operations with SEPIA (2012, methodology): “3 shows the average total running time of the weighted set intersection protocol (left) and the event correlation protocol (right) presented in [7].”
DStress: Efficient Differentially Private Computations on Distributed Data (2017, methodology): “In general, systems that use BGW, such as PICCO [65] or SEPIA [15], can offer better performance for applications that mostly use arithmetic operations.”
Deploying Secure Multi-Party Computation for Financial Data Analysis - (Short Paper) (2012, background): “MPC has been studied for almost thirty years and recently, many MPC projects have started reaching practical results [9,10,1,7,13,15,4,11].”
High-performance secure multi-party computation for data mining applications (2012, methodology): “In the last few years, several MPC frameworks based on various techniques have been proposed and implemented, for example FairPlayMP [1], VIFF [12], SEPIA [6], SecureSCM [18], VMcrypt [15], TASTY [14] and Sharemind [2].”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “Some provide secure integer arithmetic [20], [21], [22], [23], [24], [25], [26], floating point arithmetic [27], [28], shuffling and sorting [29] and linking [4].”