Threshold secret sharing enables distributing a message to n parties such that no subset of fewer than t parties can learn the message, whereas any subset of at least t parties can recover the message. Despite being a fundamental primitive, secret sharing still suffers from one significant drawback, where its message reconstruction algorithm is computationally expensive for large privacy thresholds t. In this paper, we aim to address this significant drawback.

We study the problem of differentially private (DP) mechanisms for representing sets of size $k$ from a large universe. Our first construction creates $(\epsilon,\delta)$-DP representations with error probability of $1/(e^\epsilon + 1)$ using space at most $1.05 k \epsilon \cdot \log(e)$ bits where the time to construct a representation is $O(k \log(1/\delta))$ while decoding time is $O(\log(1/\delta))$. We also present a second algorithm for pure $\epsilon$-DP representations with the same error using space at most $k \epsilon \cdot \log(e)$ bits, but requiring large decoding times. Our algorithms match the lower bounds on privacy-utility trade-offs (including constants but ignoring $\delta$ factors) and we also present a new space lower bound matching our constructions up to small constant factors. To obtain our results, we design a new approach embedding sets into random linear systems deviating from most prior approaches that inject noise into non-private solutions.

We study encrypted storage schemes where a client outsources data to an untrusted third-party server (such as a cloud storage provider) while maintaining the ability to privately query and dynamically update the data. We focus on encrypted multi-maps (EMMs), a structured encryption (STE) scheme that stores pairs of label and value tuples. EMMs allow queries on labels and return the associated value tuple. As responses are variable-length, EMMs are subject to volume leakage attacks introduced by Kellaris et al. [CCS'16]. To prevent these attacks, volume-hiding EMMs were introduced by Kamara and Moataz [Eurocrypt'19] that hide the label volumes (i.e., the value tuple lengths). As our main contribution, we present the first fully dynamic volume-hiding EMMs that are both asymptotically and concretely efficient. Furthermore, they are simultaneously forward and backward private which are the de-facto standard security notions for dynamic STE schemes. Additionally, we implement our schemes to showcase their concrete efficiency. Our experimental evaluations show that our constructions are able to add dynamicity with minimal to no additional cost compared to the prior best static volume-hiding schemes of Patel et al. [CCS'19].

Private set intersection and related functionalities are among the most prominent real-world applications of secure multiparty computation. While such protocols have attracted significant attention from the research community, other functionalities are often required to support a PSI application in practice. For example, in order for two parties to run a PSI over the unique users contained in their databases, they might first invoke a support functionality to agree on the primary keys to represent their users. This paper studies a secure approach to agreeing on primary keys. We introduce and realize a functionality that computes a common set of identifiers based on incomplete information held by two parties, which we refer to as private identity agreement, and we prove the security of our protocol in the honest-but-curious model. We explain the subtleties in designing such a functionality that arise from privacy requirements when intending to compose securely with PSI protocols. We also argue that the cost of invoking this functionality can be amortized over a large number of PSI sessions, and that for applications that require many repeated PSI executions, this represents an improvement over a PSI protocol that directly uses incomplete or fuzzy matches.

In this work, we discuss our successful efforts for industry deployment of a cryptographic secure computation protocol. The problem we consider is privately computing aggregate conversion rate of advertising campaigns. This underlying functionality can be abstracted as Private Intersection-Sum (PI-Sum) with Cardinality. In this setting two parties hold datasets containing user identifiers, and one of the parties additionally has an integer value associated with each of its user identifiers. The parties want to learn the number of identifiers they have in common and the sum of the integer values associated with these users without revealing any more information about their private inputs. We identify the major properties and enabling factors which make the deployment of a cryptographic protocol possible, practical, and uniquely positioned as a solution for the task at hand. We describe our deployment setting and the most relevant efficiency measure, which in our setting is communication overhead rather than computation. We also present a monetary cost model that can be used as a unifying cost measure and the computation model which reflect out use-case: a low-priority batch computing. We present three PI-Sum with cardinality protocols: our currently deployed protocol, which relies on a Diffie-Hellman style double masking, and two new protocols which leverage more recent techniques for private set intersection (PSI) that use Random Oblivious Transfer and encrypted Bloom filters. We compare the later two protocol with our original solution when instantiated with different additively homomorphic encryption schemes. We implement our constructions and compare their costs. We also compare with recent generic approaches for computing on the intersection of two datasets and show that our best protocol has monetary cost that is 20× less than the best known generic approach.

Volume leakage has recently been identified as a major threat to the security of cryptographic cloud-based data structures by Kellaris \em et al. [CCS'16] (see also the attacks in Grubbs \em et al. [CCS'18] and Lacharité \em et al. [S&P'18]). In this work, we focus on volume-hiding implementations of \em encrypted multi-maps as first considered by Kamara and Moataz [Eurocrypt'19]. Encrypted multi-maps consist of outsourcing the storage of a multi-map to an untrusted server, such as a cloud storage system, while maintaining the ability to perform private queries. Volume-hiding encrypted multi-maps ensure that the number of responses (volume) for any query remains hidden from the adversarial server. As a result, volume-hiding schemes can prevent leakage attacks that leverage the adversary's knowledge of the number of query responses to compromise privacy. We present both conceptual and algorithmic contributions towards volume-hiding encrypted multi-maps. We introduce the first formal definition of volume-hiding leakage functions. In terms of design, we present the first volume-hiding encrypted multi-map dprfMM whose storage and query complexity are both asymptotically optimal. Furthermore, we experimentally show that our construction is practically efficient. Our server storage is smaller than the best previous construction while we improve query complexity by a factor of 10-16x. In addition, we introduce the notion of differentially private volume-hiding leakage functions which strikes a better, tunable balance between privacy and efficiency. To accompany our new notion, we present a differentially private volume-hiding encrypted multi-map dpMM whose query complexity is the volume of the queried key plus an additional logarithmic factor. This is a significant improvement compared to all previous volume-hiding schemes whose query overhead was the maximum volume of any key. In natural settings, our construction improves the average query overhead by a factor of 150-240x over the previous best volume-hiding construction even when considering small privacy budget of ε=0.2.

Oblivious RAM (ORAM) and private information retrieval (PIR) are classic cryptographic primitives used to hide the access pattern to data whose storage has been outsourced to an untrusted server. Unfortunately, both primitives require considerable overhead compared to plaintext access. For large-scale storage infrastructure with highly frequent access requests, the degradation in response time and the exorbitant increase in resource costs incurred by either ORAM or PIR prevent their usage. In an ideal scenario, a privacy-preserving storage protocols with small overhead would be implemented for these heavily trafficked storage systems to avoid negatively impacting either performance and/or costs. In this work, we study the problem of the best \em storage access privacy that is achievable with only \em small overhead over plaintext access. To answer this question, we consider \em differential privacy access which is a generalization of the \em oblivious access security notion that are considered by ORAM and PIR. Quite surprisingly, we present strong evidence that constant overhead storage schemes may only be achieved with privacy budgets of ε = Ømega(łog n)$. We present asymptotically optimal constructions for differentially private variants of both ORAM and PIR with privacy budgets ε = Θ(łog n)$ with only $O(1)$ overhead. In addition, we consider a more complex storage primitive called key-value storage in which data is indexed by keys from a large universe (as opposed to consecutive integers in ORAM and PIR). We present a differentially private key-value storage scheme with ε = Θ(łog n)$ and $O(łogłog n)$ overhead. This construction uses a new oblivious, two-choice hashing scheme that may be of independent interest.

Private information retrieval (PIR) is a fundamental tool for preserving query privacy when accessing outsourced data. All previous PIR constructions have significant costs preventing widespread use. In this work, we present private stateful information retrieval (PSIR), an extension of PIR, allowing clients to be stateful and maintain information between multiple queries. Our design of the PSIR primitive maintains three important properties of PIR: multiple clients may simultaneously query without complex concurrency primitives, query privacy should be maintained if the server colludes with other clients, and new clients should be able to enroll into the system by exclusively interacting with the server. We present a PSIR framework that reduces an online query to performing one single-server PIR on a sub-linear number of database records. All other operations beyond the single-server PIR consist of cryptographic hashes or plaintext operations. In practice, the dominating costs of resources occur due to the public-key operations involved with PIR. By reducing the input database to PIR, we are able to limit expensive computation and avoid transmitting large ciphertexts. We show that various instantiations of PSIR reduce server CPU by up to 10x and online network costs by up to 10x over the previous best PIR construction.

We present PanORAMa, the first Oblivious RAM construction that achieves communication overhead O(log N log log N) for database of N blocks and for any block size B = Ω(log N) while requiring client memory of only a constant number of memory blocks. Our scheme can be instantiated in the "balls and bins" model in which Goldreich and Ostrovsky [JACM 96] showed an Ω(log N) lower bound for ORAM communication. Our construction follows the hierarchical approach to ORAM design and relies on two main building blocks of independent interest: a new oblivious hash table construction with improved amortized O(log N + poly(log log λ)) communication overhead for security parameter λ and N = poly(λ), assuming its input is randomly shuffled; and a complementary new oblivious random multi-array shuffle construction, which shuffles N blocks of data with communication O(N log log λ + N log N/log λ) when the input has a certain level of entropy. We combine these two primitives to improve the shuffle time in our hierarchical ORAM construction by avoiding heavy oblivious shuffles and leveraging entropy remaining in the merged levels from previous shuffles. As a result, the amortized shuffle cost is asymptotically the same as the lookup complexity in our construction.

We consider Oblivious Shuffling and K-Oblivious Shuffling, a refinement thereof. We provide efficient algorithms for both and discuss their application to the design of Oblivious RAM. The task of K-Oblivious Shuffling is to obliviously shuffle N encrypted blocks that have been randomly allocated on the server in such a way that an adversary learns nothing about the new allocation of blocks. The security guarantee should hold also with respect to an adversary that has learned the initial position of K touched blocks out of the N blocks. The classical notion of Oblivious Shuffling is obtained for K = N. We present a family of algorithms for Oblivious Shuffling. Our first construction, CacheShuffleRoot, is tailored for clients with $O(\sqrt{N})$ blocks of memory and uses $(4+ε)N$ blocks of bandwidth, for every $ε> 0$. CacheShuffleRoot is a 4.5x improvement over previous best known results on practical sizes of N. We also present CacheShuffle that obliviously shuffles using O(S) blocks of client memory with $O(N\log_S N)$ blocks of bandwidth. We then turn to K-Oblivious Shuffling and give algorithms that require 2N + f(K) blocks of bandwidth, for some function f. That is, any extra bandwidth above the 2N lower bound depends solely on K. We present KCacheShuffleBasic that uses O(K) client storage and exactly 2N blocks of bandwidth. For smaller client storage requirements, we show KCacheShuffle, which uses O(S) client storage and requires $2N+(1+ε)O(K\log_S K)$ blocks of bandwidth. Finally, we consider the case in which, in addition to the N blocks, the server stores D dummy blocks whose content is is irrelevant but still their positions must be hidden by the shuffling. For this case, we design algorithm KCacheShuffleDummy that, for N + D blocks and K touched blocks, uses O(K) client storage and $D+(2+ε)N$ blocks of bandwidth.

We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers $1.73 x communication expansion for 210 users and 220-dimensional vectors, and 1.98 x expansion for 214 users and 224-dimensional vectors over sending data in the clear.

Packet data is expected to dominate third generation wireless networks, unlike current generation voice networks. This opens up new and interesting problems. Physical and link layer issues have been studied extensively, while resource allocation and scheduling issues have not been addressed satisfactorily.

In continuous speech recognition, a significant amount of time is used every frame to evaluate interword transitions. In fact, if N is the size of the vocabulary and each word transitions on average to E other words then O(NE) operations are required. Similarly when evaluating a partially connected HMM, the Viterbi algorithm requires O(NE) operations. This paper presents the first algorithm to break the O(NE) complexity requirement. The new algorithm has an average complexity of O(N/spl radic/E). An algorithm was previously presented by the author for the special case of fully connected models, however, the new algorithm is general. It speeds up evaluations of both partial and fully connected HMM and language models. Unlike pruning, this paper does not use any heuristics which may sacrifice optimality, but fundamentally improves the basic evaluation of the time synchronous Viterbi algorithm.

Encrypted Key Exchange (EKE) (S. Bellovin and M. Merritt, 1992; 1993) allows two parties sharing a password to exchange authenticated information over an insecure network by using a combination of public and secret key cryptography. EKE promises security against active attacks and dictionary attacks. Other secure protocols have been proposed based on the use of randomized confounders (L. Gong et al., 1993). We use some basic results from number theory to present password guessing attacks on all versions of EKE discussed in the paper (S. Bellovin and M. Merritt, 1992) and we also offer countermeasures to the attacks. However for the RSA version of EKE, we show that simple modifications are not enough to rescue the protocol. Attacks are also presented on half encrypted versions of EKE. We also show how randomized confounders cannot protect Direct Authentication Protocol and Secret Public Key Protocol versions of a secure password scheme from attacks. We discuss why these attacks are possible against seemingly secure protocols and what is necessary to make secure protocols.

In continuous speech recognition, when using statistical language models (e.g. bigrams) a significant amount of time is used every frame to evaluate interword transitions. In fact, if N is the size of vocabulary, O(N/sup 2/) operations are required per frame. Also, when evaluating fully connected HMM with N states, the Viterbi algorithm requires O(N/sup 2/) operations per frame. This paper presents the first algorithm to break the O(N/sup 2/) complexity requirement in the Viterbi algorithm, whether evaluating interword transitions or evaluating a fully connected HMM. The algorithm presented has an average complexity of O(N/spl radic/N). Previous speed-ups of the evaluations of interword transitions used heuristics, like pruning, or relied upon unavailability of many of the bigram values. However, this paper does not rely on any heuristics but fundamentally improves the basic evaluation of the time synchronous Viterbi algorithm.