Mobile edge computing (MEC) is a promising solution for enhancing user experience, minimizing content delivery expenses, and reducing backhaul traffic. This paper presents a game-theoretic framework to address the edge resource crowdsourcing problem, where mobile edge devices (MEDs) provide idle storage for content caching in exchange for rewards from a content provider (CP). We model the interaction between the CP and MEDs as a Stackelberg game, with the CP as the leader setting the reward structure and the MEDs as followers competing in a non-cooperative game for these rewards. We propose a novel privacy-preserving method to derive the Stackelberg equilibrium of the game. Notably, our algorithm is designed to operate effectively in time-varying communication networks, addressing the high mobility inherent in MEC environments. This contrasts with state-of-the-art algorithms, which assume a static communication network among MEDs–an impractical condition that does not account for the mobility of MEDs during algorithm execution. Specifically, our approach employs consensus-based algorithms to compute the Nash equilibrium (NE) for MEDs, with MEDs exchanging NE profile estimates with neighbors via row-stochastic mixing matrices and performing gradient steps to optimize their utility in a fully decentralized manner. Based on the computed NE strategies, we propose a zeroth-order reward search algorithm for the CP to determine the optimal strategy for profit maximization. Our comprehensive analysis details the properties of the equilibrium and establishes the geometric convergence of the proposed algorithms to the NE. We also derive explicit bounds for the stepsizes based on the game's properties and the graphs' connectivity structure. Extensive numerical results validate the efficacy of our proposed approach.

Multi-party private set union (mPSU) allows multiple parties to compute the union of their private input sets without revealing any additional information. Existing efficient mPSU protocols can be categorized into symmetric key encryption (SKE)-based and public key encryption (PKE)-based approaches. However, neither type of mPSU protocol scales efficiently to a large number of parties, as they fail to fully utilize available computational resources, leaving participants idle during various stages of the protocol execution.

A two-party fuzzy private set intersection (PSI) protocol between Alice and Bob with input sets A and B allows Alice to learn nothing more than the points of Bob that are ''δ-close'' to its points in some metric space dist . More formally, Alice learns only the set {b | dist (a,b) ≤ δ, a ∈ A, b ∈ B} for a predefined threshold δ and distance metric dist, while Bob learns nothing about Alice's set. Fuzzy PSI is a valuable privacy tool in scenarios where private set intersection needs to be computed over imprecise or measurement-based data, such as GPS coordinates or healthcare data. Previous approaches to fuzzy PSI rely on asymmetric cryptographic primitives, generic two-party computation (2PC) techniques like garbled circuits, or function secret sharing methods, all of which are computationally intensive and lead to poor concrete efficiency. This work introduces a new modular framework for fuzzy PSI, primarily built on efficient symmetric key primitives. Our framework reduces the design of efficient fuzzy PSI to a novel variant of oblivious transfer (OT), which we term distance-aware random OT (da-ROT). This variant enables the sender to obtain two random strings (r0, r1), while the receiver obtains one of these values rb, depending on whether the receiver's input keyword a and the sender's input keyword b are close in some metric space i.e., dist (a,b) ≤ δ. The da-ROT can be viewed as a natural extension of traditional OT, where the condition (choice bit) is known to the receiver. We propose efficient constructions for da-ROT based on standard OT techniques tailored for small domains, supporting distance metrics such as the Chebyshev norm, the Euclidean norm, and the Manhattan norm. By integrating these da-ROT constructions, our fuzzy PSI framework achieves up to a 14× reduction in communication cost and up to a 54× reduction in computation cost compared to previous state-of-the-art protocols, across input set sizes ranging from 28 to 216. Additionally, we extend our framework to compute fuzzy PSI cardinality and fuzzy join from traditional PSI-related functionalities. All proposed protocols are secure in the semi-honest model.

Federated Learning (FL) enables multiple clients to collaboratively train a machine learning model while keeping their data private, eliminating the need for data sharing. Two common approaches to secure aggregation (SA) in FL are the single-aggregator and multiple-aggregator models. This work focuses on improving the multiple-aggregator model.Existing multiple-aggregator protocols such as Prio (NSDI 2017), Prio+ (SCN 2022), Elsa (S&P 2023) either offer robustness only in the presence of semi-honest servers or provide security without robustness and are limited to two aggregators. We introduce Mario, the first multiple-aggregator Secure Aggregation protocol that is both secure and robust in a malicious setting. Similar to prior work of Prio and Prio+, Mario provides secure aggregation in a setup of n servers and m clients. Unlike previous work, Mario removes the assumption of semi-honest servers, and provides a complete protocol with robustness under malicious clients and malicious servers. Our implementation shows that Mario is 3.40× and 283.4× faster than Elsa and Prio+, respecitively.

DNA edit distance (ED) measures the minimum number of single nucleotide insertions, substitutions, or deletions required to convert a DNA sequence into another. ED has broad applications in healthcare such as sequence alignment, genome assembly, functional annotation, and drug discovery. Privacy-preserving computation is essential in this context to protect sensitive genomic data. Nonetheless, the existing secure DNA edit distance solutions lack efficiency when handling large data sequences or resort to approximations and fail to accurately compute the metric. In this work, we introduce ScureED, a protocol that tackles these limitations, resulting in a significant performance enhancement of approximately 2-24 times compared to existing methods. Our protocol computes a secure ED between two genomes, each comprising 1,000 letters, in just a few seconds. The underlying technique of our protocol is a novel approach that transforms the established approximate matching technique (i.e., the Ukkonen algorithm) into exact matching, exploiting the inherent similarity in human DNA to achieve cost-effectiveness. Furthermore, we introduce various optimizations tailored for secure computation in scenarios with a limited input domain, such as DNA sequences composed solely of the four nucleotide letters.

Generative models have achieved remarkable success in a wide range of applications. Training such models using proprietary data from multiple parties has been studied in the realm of federated learning. Yet recent studies showed that reconstruction of authentic training data can be achieved in such settings. On the other hand, multiparty computation (MPC) guarantees standard data privacy, yet scales poorly for training generative models. In this paper, we focus on improving reconstruction hardness during Generative Adversarial Network (GAN) training while keeping the training cost tractable. To this end, we explore two training protocols that use a public generator and an MPC discriminator: Protocol 1 (P1) uses a fully private discriminator, while Protocol 2 (P2) privatizes the first three discriminator layers. We prove reconstruction hardness for P1 and P2 by showing that (1) a public generator does not allow recov- ery of authentic training data, as long as the first two layers of the discriminator are private; and through an existing approximation hardness result on ReLU networks, (2) a discriminator with at least three private layers does not allow authentic data reconstruction with algorithms polynomial in network depth and size. We show empirically that compared with fully MPC training, P1 reduces the training time by 2× and P2 further by 4 − 16×. Our implementation can be found at https://github.com/asu-crypto/ppgan.

In aggregation queries, predicate parameters often reveal user intent. Protecting these parameters is critical for user privacy, regardless of whether the database is public or private. While most existing works focus on private data settings, we address a public data setting where the server has access to the database. Current solutions for this setting either require additional setups (e.g., non-colluding servers, hardware enclaves) or are inefficient for practical workloads. Furthermore, they often do not support range predicates or boolean combinations commonly seen in real-world use cases. To address these limitations, we built HADES, a fully homomorphic encryption (FHE) based private aggregation system for public data that supports point, range predicates, and boolean combinations. Our one-round HADES protocol efficiently generates predicate indicators by leveraging the plaintext form of public data records. It introduces a novel elementwise-mapping operation and an optimized reduction algorithm, achieving latency efficiency within a limited noise budget. Our highly scalable, multi-threaded implementation improves performance over previous one-round FHE solutions by 204x to 6574x on end-to-end TPC-H queries, reducing aggregation time on 1M records from 15 hours to 38 seconds.

This paper studies the joint optimization of edge node activation and resource pricing in edge computing, where an edge computing platform provides heterogeneous resources to accommodate multiple services with diverse pReferences. We cast this problem as a bi-level program, with the platform acting as the leader and the services as the followers. The platform aims to maximize net profit by optimizing edge resource prices and edge node activation, with the services’ optimization problems acting as constraints. Based on the platform’s decisions, each service aims to minimize its costs and enhance user experience through optimal service placement and resource procurement decisions. The presence of integer variables in both the upper and lower-level problems renders this problem particularly challenging. Traditional techniques for transforming bi-level problems into single-level formulations are inappropriate owing to the non-convex nature of the follower problems. Drawing inspiration from the column-and-constraint generation method in robust optimization, we develop an efficient decomposition-based iterative algorithm to compute an exact optimal solution to the formulated bi-level problem. Extensive numerical results are presented to demonstrate the efficacy of the proposed model and technique.

Researchers across various fields seek to understand causal relationships but often find controlled experiments impractical. To address this, statistical tools for causal discovery from naturally observed data have become crucial. Non-linear regression models, such as Gaussian process regression, are commonly used in causal inference but have limitations due to high costs when adapted for secure computation. Support vector regression (SVR) offers an alternative but remains costly in an Multi-party computation context due to conditional branches and support vector updates.

This paper studies a multi-party private set union (mPSU), a fundamental cryptographic problem that allows multiple parties to compute the union of their respective datasets without revealing any additional information. We propose an efficient mPSU protocol which is secure in the presence of any number of colluding semi-honest participants. Our protocol avoids computationally expensive homomorphic operations or generic multi-party computation, thus providing an efficient solution for mPSU. The crux of our protocol lies in the utilization of new cryptographic tool, namely, Membership Oblivious Transfer (mOT). We believe that the mOT may be of independent interest. We implement our mPSU protocol and evaluate its performance. Our protocol shows an improvement of up to $80.84 times$ in terms of running time and $405.73 times$ bandwidth cost compared to the existing state-of-the-art protocols.

We describe a new paradigm for multi-party private set intersection cardinality (PSI-CA) that allows $n$ parties to compute the intersection size of their datasets without revealing any additional information. We explore a variety of instantiations of this paradigm. By operating under the assumption that a particular subset of parties refrains from collusion, our protocols avoid computationally expensive public-key operations and are secure in the presence of a semi-honest adversary. We demonstrate the practicality of our PSI-CA with an implementation. For $n=16$ parties with data-sets of $2^{20}$ items each, our server-aided variant takes 71 seconds. Interestingly, in the server-less setting, the same task takes only 7 seconds. To the best of our knowledge, this is the first `special purpose' implementation of a multi-party PSI-CA from symmetric-key techniques (i.e. an implementation that does not rely on a generic underlying MPC).We study two interesting applications -- heatmap computation and associated rule learning (ARL) -- that can be computed securely using a dot-product as a building block. We analyse the performance of securely computing heatmap and ARL using our protocol and compare that to the state-of-the-art.

Private matching for compute (PMC) establishes a match between two datasets owned by mutually distrusted parties (C and P) and allows the parties to input more data for the matched records for arbitrary downstream secure computation without rerunning the private matching component. The state-of-the-art PMC protocols only support two parties and assume that both parties can participate in computationally intensive secure computation. We observe that such operational overhead limits the adoption of these protocols to solely powerful entities as small data owners or devices with minimal computing power will not be able to participate. We introduce two protocols to delegate PMC from party P to untrusted cloud servers, called delegates, allowing multiple smaller P parties to provide inputs containing identifiers and associated values. Our Delegated Private Matching for Compute protocols, called DPMC and DsPMC, establish a join between the datasets of party C and multiple delegators P based on multiple identifiers and compute secret shares of associated values for the identifiers that the parties have in common. We introduce a rerandomizable encrypted oblivious pseudorandom function (OPRF) primitive, called EO, which allows two parties to encrypt, mask, and shuffle their data. Note that EO may be of independent interest. Our DsPMC protocol limits the leakages of DPMC by combining our EO scheme and secure three-party shuffling. Finally, our implementation demonstrates the efficiency of our constructions by outperforming related works by approximately 10x for the total protocol execution and by at least 20x for the computation on the delegators.

Abstract Contact tracing is an essential tool for controlling the spread of disease through human populations. However, existing contact tracing applications are either vulnerable to privacy and security attacks or heavy bandwidth/computational requirements on the client's devices. In this work, we introduce SecureCT , a Secure Contact Tracing platform with strong privacy protection and lightweight cost. SecureCT prevents linkage attacks, eliminates replay and relay attacks, and allows the phone's holder to delegate their contact tracing computation to untrusted servers while maintaining the user's privacy. The technical core of our scheme is an efficient Private Set Intersection Cardinality protocol which only relies on symmetric‐key primitives. We evaluate its performance to show the feasibility of our proposed system in practice.

The COVID19 pandemic is having a worldwide impact on the way business is conducted, people interact, work is organised, and more. In a line, it is changing our way of life. In this Special Issue: COVID-19 and Cybersecurity, we focus on the many ramifications of COVID-19 into the Cybersecurity realm. Other Information Published in: IET Information Security License: http://creativecommons.org/licenses/by-nc/4.0/ See article on publisher's website: https://dx.doi.org/10.1049/ise2.12084

Edge computing has emerged as a key technology to reduce network traffic, improve user experience, and enable numerous Internet of Things applications. In this article, we study an optimal resource procurement problem for a service provider (SP), who can purchase resources from various edge nodes in the edge computing market to serve its users’ requests. How to jointly optimize the service placement, resource sizing, and workload allocation decisions is a challenging problem, which becomes even more complicated when considering demand uncertainty. To this end, we propose a novel two-stage adaptive robust optimization framework to help the SP optimally determine the locations for installing its service (i.e., placement) and the amount of computing resource to purchase from each location (i.e., sizing). The proposed placement and sizing solution can hedge against any possible realization within a predefined demand uncertainty set. Given the first-stage robust solution, the optimal resource and workload allocation decisions are computed in the second stage after the uncertainty is revealed. To solve the two-stage model, this article presents an iterative solution approach by employing the column-and-constraint generation method that decomposes the underlying problem into a master problem and a max–min subproblem associated with the second stage. Extensive numerical results are shown to illustrate the efficacy of the proposed model.

We address the problem of multiparty private set intersection against a malicious adversary. First, we show that when one can assume no collusion amongst corrupted parties then there exists an extremely efficient protocol given only symmetric-key primitives. Second, we present a protocol secure against an adversary corrupting any strict subset of the parties. Our protocol is based on the recently introduced primitives: oblivious programmable PRF (OPPRF) and oblivious key-value store (OKVS).

We describe a protocol for two-party private set intersection (PSI) based on Diffie-Hellman key agreement. The protocol is proven secure against malicious parties, in the ideal permutation + random oracle model.

Abstract Clustering is a common technique for data analysis, which aims to partition data into similar groups. When the data comes from different sources, it is highly desirable to maintain the privacy of each database. In this work, we study a popular clustering algorithm (K-means) and adapt it to the privacypreserving context. Specifically, to construct our privacy-preserving clustering algorithm, we first propose an efficient batched Euclidean squared distance computation protocol in the amortizing setting, when one needs to compute the distance from the same point to other points. Furthermore, we construct a customized garbled circuit for computing the minimum value among shared values.We believe these new constructions may be of independent interest. We implement and evaluate our protocols to demonstrate their practicality and show that they are able to train datasets that are much larger and faster than in the previous work. The numerical results also show that the proposed protocol achieve almost the same accuracy compared to a K-means plain-text clustering algorithm.

Abstract An important initialization step in many social-networking applications is contact discovery, which allows a user of the service to identify which of its existing social contacts also use the service. Naïve approaches to contact discovery reveal a user’s entire set of social/professional contacts to the service, presenting a significant tension between functionality and privacy. In this work, we present a system forprivatecontact discovery, in which the client learnsonlythe intersection of its own contact list and a server’s user database, and the server learns only the (approximate) size of the client’s list. The protocol is specifically tailored to the case of a small client set and large user database. Our protocol has provable security guarantees and combines new ideas with state-of-the-art techniques from private information retrieval and private set intersection. We report on a highly optimized prototype implementation of our system, which is practical on real-world set sizes. For example, contact discovery between a client with 1024 contacts and a server with 67 million user entries takes 1.36 sec (when using server multi-threading) and uses only 4.28 MiB of communication.

We present a new paradigm for multi-party private set intersection (PSI) that allows $n$ parties to compute the intersection of their datasets without revealing any additional information. We explore a variety of instantiations of this paradigm. Our protocols avoid computationally expensive public-key operations and are secure in the presence of any number of semi-honest participants (i.e., without an honest majority).

Cut-and-choose (CC) is the standard approach to making Yao's garbled circuit two-party computation (2PC) protocol secure against malicious adversaries. Traditional cut-and-choose operates at the level of entire circuits, whereas the LEGO paradigm (Nielsen & Orlandi, TCC 2009) achieves asymptotic improvements by performing cut-and-choose at the level of individual gates. In this work we propose a unified approach called DUPLO that spans the entire continuum between these two extremes. The cut-and-choose step in our protocol operates on the level of arbitrary circuit "components," which can range in size from a single gate to the entire circuit itself.

We describe a lightweight protocol for oblivious evaluation of a pseudorandom function (OPRF) in the presence of semihonest adversaries. In an OPRF protocol a receiver has an input r; the sender gets output s and the receiver gets output F(s; r), where F is a pseudorandom function and s is a random seed. Our protocol uses a novel adaptation of 1-out-of-2 OT-extension protocols, and is particularly efficient when used to generate a large batch of OPRF instances. The cost to realize m OPRF instances is roughly the cost to realize 3:5m instances of standard 1-out-of-2 OTs (using state-of-the-art OT extension). We explore in detail our protocol's application to semihonest secure private set intersection (PSI). The fastest state-of- the-art PSI protocol (Pinkas et al., Usenix 2015) is based on efficient OT extension. We observe that our OPRF can be used to remove their PSI protocol's dependence on the bit-length of the parties' items. We implemented both PSI protocol variants and found ours to be 3.1{3.6 faster than Pinkas et al. for PSI of 128-bit strings and sufficiently large sets. Concretely, ours requires only 3.8 seconds to securely compute the intersection of 220-size sets, regardless of the bitlength of the items. For very large sets, our protocol is only 4:3 slower than the insecure naive hashing approach for PSI.