Efficient Private Matching for Private Databases

Private matching (PM) is a key cryptographic primitive in secure computation that allows several parties to jointly compute some functions depending on their private inputs. Indeed, this primitive has many practical applications. For instance, in online advertising, two companies may wish to find their common customers for a joint marketing campaign. In this scenario, privacy is of utmost importance and it is imperative to ensure that neither company can learn more than their own data and the results of the match. In recent years, secure computation in general and PM in particular has attracted considerable attention from the research community, partly due to the rise of Big Data along with the ever-increasing privacy concerns. This thesis describes three secure private set intersection (PSI) protocols, one private set union (PSU) construction, and one pattern matching scheme. PM can be considered as a main building block in all these protocols. To securely compute the intersection of two sets of size $2^{20}$ our proposed protocols require only 3 seconds which is $4\times$ faster than the previous best protocol. In the multi-party setting, we provide the first implementation that takes only 72 seconds to compute PSI for 5 parties with data-sets of $2^{20}$ items each. For private set union (PSU), our protocol improves prior work by a factor up to $7,600 \times$ for large instances. In addition, our wild-card pattern matching (WPM) protocol shows over two orders of magnitude faster than the state-of-the-art scheme

Quantum-based Distributed Algorithms for Edge Node Placement and Workload Allocation

Edge computing is a promising technology that offers a superior user experience and enables various innovative Internet of Things applications. In this paper, we present a mixed-integer linear programming (MILP) model for optimal edge server placement and workload allocation, which is known to be NP-hard. To this end, we explore the possibility of addressing this computationally challenging problem using quantum computing. However, existing quantum solvers are limited to solving unconstrained binary programming problems. To overcome this obstacle, we propose a hybrid quantum-classical solution that decomposes the original problem into a quadratic unconstrained binary optimization (QUBO) problem and a linear program (LP) subproblem. The QUBO problem can be solved by a quantum solver, while the LP subproblem can be solved using traditional LP solvers. Our numerical experiments demonstrate the practicality of leveraging quantum supremacy to solve complex optimization problems in edge computing

Compact and Malicious Private Set Intersection for Small Sets

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. For small sets (500 items or fewer), our protocol requires the least time and communication of any known PSI protocol, even ones that are only semi-honest secure and ones that are not based on Diffie-Hellman. It is one of the few significant improvements to the 20-year old classical Diffie-Hellman PSI protocol of Huberman, Franklin, and Hogg (ACM Elec. Commerce 1999). Our protocol is actually a generic transformation that constructs PSI from a class of key agreement protocols. This transformation is inspired by a technique of Cho, Dachman-Soled, and Jarecki (CT-RSA 2016), which we streamline and optimize in several important ways to achieve our superior efficiency

MPCCache: Privacy-Preserving Multi-Party Cooperative Cache Sharing at the Edge

Edge computing and caching have emerged as key technologies in the future communication network to enhance the user experience, reduce backhaul traffic, and enable various Internet of Things applications. Different from conventional resources like CPU and memory that can be utilized by only one party at a time, a cached data item, which can be considered as a public good, can serve multiple parties simultaneously. Therefore, instead of independent caching, it is beneficial for the parties (e.g., Telcos) to cooperate and proactively store their common items in a shared cache that can be accessed by all the parties at the same time. In this work, we present MPCCache, a novel privacy-preserving Multi-party Cooperative Cache sharing framework, which allows multiple network operators to determine a set of common data items with the highest access frequencies to be stored in their capacity-limited shared cache while guaranteeing the privacy of their individual datasets. The technical core of our MPCCache is a new construction that allows multiple parties to compute a specific function on the intersection set of their datasets, without revealing the intersection itself to any party. We evaluate our protocols to demonstrate their practicality and show that MPCCache scales well to large datasets and achieves a few hundred times faster compared to a baseline scheme that optimally combines existing MPC protocols

Optimal Workload Allocation for Distributed Edge Clouds With Renewable Energy and Battery Storage

This paper studies an optimal workload allocation problem for a network of renewable energy-powered edge clouds that serve users located across various geographical areas. Specifically, each edge cloud is furnished with both an on-site renewable energy generation unit and a battery storage unit. Due to the discrepancy in electricity pricing and the diverse temporal-spatial characteristics of renewable energy generation, how to optimally allocate workload to different edge clouds to minimize the total operating cost while maximizing renewable energy utilization is a crucial and challenging problem. To this end, we introduce and formulate an optimization-based framework designed for Edge Service Providers (ESPs) with the overarching goal of simultaneously reducing energy costs and environmental impacts through the integration of renewable energy sources and battery storage systems, all while maintaining essential quality-of-service standards. Numerical results demonstrate the effectiveness of the proposed model and solution in maintaining service quality as well as reducing operational costs and emissions. Furthermore, the impacts of renewable energy generation and battery storage on optimal system operations are rigorously analyzed

Simple, Fast Malicious Multiparty Private Set Intersection

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). Our protocols follow the client-server model where each party is either a client or a server. However, in contrast to previous works where the client has to engage in an expensive interactive cryptographic protocol, our clients need only send a single key to each server and a single message to a {\em pivot} party (where message size is in the order of the set size). Our experiments show that the client\u27s load improves by up to $10 \times$ (compared to both semi-honest and malicious settings) and that factor increases with the number of parties. We implemented our protocol and conducted an extensive experiment over both LAN and WAN and up to 32 parties with up to $2^{20}$ items each. We provide a comparison of the performance of our protocol and the state-of-the-art for both the semi-honest setting (by Chandran et al.) and the malicious setting (by Ben Efraim et al. and Garimella et al.)

Multiparty Private Set Intersection Cardinality and Its Applications

We describe a new paradigm for multi-party private set intersection cardinality (\psica) 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. 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 \psica\ 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\u27 implementation of a multi-party \psica\ 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

Toward A Practical Multi-party Private Set Union

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 tools, namely, Membership Oblivious Transfer (mOT) and Conditional Oblivious Pseudorandom Function (cOPRF). We believe that the mOT and cOPRF may be of independent interest. We implement our mPSU protocol and evaluate their performance. Our protocol shows an improvement of up to $55\times$ and $776.18\times$ bandwidth cost compared to the existing state-of-the-art protocols