Block Sensitivity versus Sensitivity

Sensitivity and block sensitivity are useful and well-studied measures of computational complexity, but in spite of their similarities, the largest possible gap between them is still unknown. Rubinstein showed that this gap must be at least quadratic, and Kenyon and Kutin showed that it is at worst exponential, but many strongly suspect that the gap is indeed quadratic, or at worst polynomial. Our work shows that for a large class of functions, which includes Rubinstein\u27s function, the quadratic gap between sensitivity and block sensitivity is the best we can possibly do

Anonymous Counting Tokens

We introduce a new primitive called anonymous counting tokens (ACTs) which allows clients to obtain blind signatures or MACs (aka tokens) on messages of their choice, while at the same time enabling issuers to enforce rate limits on the number of tokens that a client can obtain for each message. Our constructions enforce that each client will be able to obtain only one token per message and we show a generic transformation to support other rate limiting as well. We achieve this new property while maintaining the unforgeability and unlinkability properties required for anonymous tokens schemes. We present four ACT constructions with various trade-offs for their efficiency and underlying security assumptions. One construction uses factorization-based primitives and a cyclic group. It is secure in the random oracle model under the q-DDHI assumption (in a cyclic group) and the DCR assumption. Our three other constructions use bilinear maps: one is secure in the standard model under q-DDHI and SXDH, one is secure in the random oracle model under SXDH, and the most efficient of the three is secure in the random oracle model and generic bilinear group model

Indistinguishability Obfuscation from Semantically-Secure Multilinear Encodings

We define a notion of semantic security of multilinear (a.k.a. graded) encoding schemes, which stipulates security of class of algebraic ``decisional\u27\u27 assumptions: roughly speaking, we require that for every nuPPT distribution $D$ over two \emph{constant-length} sequences $\vec{m}_0,\vec{m}_1$ and auxiliary elements $\vec{z}$ such that all arithmetic circuits (respecting the multilinear restrictions and ending with a zero-test) are \emph{constant} with overwhelming probability over $(\vec{m}_b, \vec{z})$, $b \in \{0,1\}$, we have that encodings of $\vec{m}_0, \vec{z}$ are computationally indistinguishable from encodings of $\vec{m}_1, \vec{z}$. Assuming the existence of semantically secure multilinear encodings and the LWE assumption, we demonstrate the existence of indistinguishability obfuscators for all polynomial-size circuits. We additionally show that if we assume subexponential hardness, then it suffices to consider a \emph{single} (falsifiable) instance of semantical security (i.e., that semantical security holds w.r.t to a particular distribution $D$) to obtain the same result. We rely on the beautiful candidate obfuscation constructions of Garg et al (FOCS\u2713), Brakerski and Rothblum (TCC\u2714) and Barak et al (EuroCrypt\u2714) that were proven secure only in idealized generic multilinear encoding models, and develop new techniques for demonstrating security in the standard model, based only on semantic security of multilinear encodings (which trivially holds in the generic multilinear encoding model). We also investigate various ways of defining an ``uber assumption\u27\u27 (i.e., a super-assumption) for multilinear encodings, and show that the perhaps most natural way of formalizing the assumption that ``any algebraic decision assumption that holds in the generic model also holds against nuPPT attackers\u27\u27 is false

Indistinguishability Obfuscation with Non-trivial Efficiency

It is well known that *inefficient* indistinguishability obfuscators (iO) with running time poly(|C|,lambda) . 2^n, where C is the circuit to be obfuscated, lambda is the security parameter, and n is the input length of C, exists *unconditionally*: simply output the function table of C (i.e., the output of C on all possible inputs). Such inefficient obfuscators, however, are not useful for applications. We here consider iO with a slightly ``non-trivial\u27\u27 notion of efficiency: the running-time of the obfuscator may still be ``trivial\u27\u27 (namely, poly(|C|,lambda) . 2^n), but we now require that the obfuscated code is just slightly smaller than the truth table of C (namely poly(|C|,lambda) . 2^{n(1-epsilon)}, where epsilon >0); we refer to this notion as *iO with exponential efficiency*, or simply *exponentially-efficient iO (XiO)*. We show that, perhaps surprisingly, under the subexponential LWE assumption, subexponentially-secure XiO for polynomial-size circuits implies (polynomial-time computable) iO for all polynomial-size circuits

Output-Compressing Randomized Encodings and Applications

We consider randomized encodings (RE) that enable encoding a Turing machine Pi and input x into its ``randomized encoding\u27\u27 \hat{Pi}(x) in sublinear, or even polylogarithmic, time in the running-time of Pi(x), independent of its output length. We refer to the former as sublinear RE and the latter as compact RE. For such efficient RE, the standard simulation-based notion of security is impossible, and we thus consider a weaker (distributional) indistinguishability-based notion of security: Roughly speaking, we require indistinguishability of \hat{Pi}_0(x_0) and \hat{Pi}_0(x_1) as long as Pi_0,x_0 and Pi_1,x_1 are sampled from some distributions such that Pi_0(x_0),Time(Pi_0(x_0)) and Pi_1(x_1),Time(Pi_1(x_1)) are indistinguishable. We first observe that compact RE is equivalent to a variant of the notion of indistinguishability obfuscation (iO)---which we refer to as puncturable iO---for the class of Turing machines without inputs. For the case of circuits, puncturable iO and iO are equivalent (and this fact is implicitly used in the powerful ``punctured program\u27\u27 paradigm by Sahai and Waters [SW13]). We next show the following: - Impossibility in the Plain Model: Assuming the existence of subexponentially secure one-way functions, subexponentially-secure sublinear RE does not exists. (If additionally assuming subexponentially-secure iO for circuits we can also rule out polynomially-secure sublinear RE.) As a consequence, we rule out also puncturable iO for Turing machines (even those without inputs). - Feasibility in the CRS model and Applications to iO for circuits: Subexponentially-secure sublinear RE in the CRS model and one-way functions imply iO for circuits through a simple construction generalizing GGM\u27s PRF construction. Additionally, any compact (even with sublinear compactness) functional encryption essentially directly yields a sublinear RE in the CRS model, and as such we get an alternative, modular, and simpler proof of the results of [AJ15,BV15] showing that subexponentially-secure sublinearly compact FE implies iO. We further show other ways of instantiating sublinear RE in the CRS model (and thus also iO): under the subexponential LWE assumption, it suffices to have a subexponentially secure FE schemes with just sublinear ciphertext (as opposed to having sublinear encryption time). - Applications to iO for Unbounded-input Turing machines: Subexponentially-secure compact RE for natural restricted classes of distributions over programs and inputs (which are not ruled out by our impossibility result, and for which we can give candidate constructions) imply iO for unbounded-input Turing machines. This yields the first construction of iO for unbounded-input Turing machines that does not rely on (public-coin) differing-input obfuscation

Secure Poisson Regression

We introduce the first construction for secure two-party computation of Poisson regression, which enables two parties who hold shares of the input samples to learn only the resulting Poisson model while protecting the privacy of the inputs. Our construction relies on new protocols for secure fixed-point exponentiation and correlated matrix multiplications. Our secure exponentiation construction avoids expensive bit decomposition and achieves orders of magnitude improvement in both online and offline costs over state of the art works. As a result, the dominant cost for our secure Poisson regression are matrix multiplications with one fixed matrix. We introduce a new technique, called correlated Beaver triples, which enables many such multiplications at the cost of roughly one matrix multiplication. This further brings down the cost of secure Poisson regression. We implement our constructions and show their extreme efficiency. In a LAN setting, our secure exponentiation for 20-bit fractional precision takes less than 0.07ms with a batch-size of 100,000. One iteration of secure Poisson regression on a dataset with 10,000 samples with 1000 binary features needs about 65.82s in the offline phase, 55.14s in the online phase and 17MB total communication. For several real datasets this translates into training that takes seconds and only a couple of MB communication

Communication-Efficient Secure Logistic Regression

We present a new construction for secure logistic regression training, which enables two parties to train a model on private secret-shared data. Our goal is to minimize online communication and round complexity, while still allowing for an efficient offline phase. As part of our construction we develop many building blocks of independent interest. These include a new approximation technique for the sigmoid function, which results in a secure protocol with better communication; secure spline evaluation and secure powers computation protocols for fixed-point values; and a new comparison protocol that optimizes online communication. We also present a new two-party protocol for generating keys for distributed point functions (DPFs) over arithmetic sharing, where previous constructions do this only for Boolean outputs. We implement our protocol in an end-to-end system and benchmark its efficiency. We can securely evaluate a sigmoid in $18$ ms online time and $0.5$ KB of online communication. Our system can train a model over a database with $70,000$ samples and $15$ features with online communication of $208.09$ MB and online time of $2.24$ hours at the cost of $6.11$c over WAN. Our benchmarks demonstrate that we reduce online communication over state of the art by $\approx 10 \times$ for sigmoid and $\approx38\times$ for logistic regression training

Private Join and Compute from PIR with Default

The private join and compute (PJC) functionality enables secure computation over data distributed across different databases, which is a functionality with a wide range of applications, many of which address settings where the input databases are of significantly different sizes. We introduce the notion of private information retrieval (PIR) with default, which enables two-party PJC functionalities in a way that hides the size of the intersection of the two databases and incurs sublinear communication cost in the size of the bigger database. We provide two constructions for this functionality, one of which requires offline linear communication, which can be amortized across queries, and one that provides sublinear cost for each query but relies on more computationally expensive tools. We construct inner-product PJC, which has applications to ads conversion measurement and contact tracing, relying on an extension of PIR with default. We evaluate the efficiency of our constructions, which can enable $\mathbf{2^{12}}$ PIR with default lookups on a database of size $\mathbf{2^{30}}$ (or inner-product PJC on databases with such sizes) with the communication of $\mathbf{945}$MB, which costs less than $\mathbf{\$0.04}$ for the client and $\mathbf{\$5.22}$ for the server

Communication--Computation Trade-offs in PIR

We study the computation and communication costs and their possible trade-offs in various constructions for private information retrieval (PIR), including schemes based on homomorphic encryption and the Gentry--Ramzan PIR (ICALP\u2705). We improve over the construction of SealPIR (S&P\u2718) using compression techniques and a new oblivious expansion, which reduce the communication bandwidth by 60% while preserving essentially the same computation cost. We then present MulPIR, a PIR protocol leveraging multiplicative homomorphism to implement the recursion steps in PIR. This eliminates the exponential dependence of PIR communication on the recursion depth due to the ciphertext expansion, at the cost of an increased computational cost for the server. Additionally, MulPIR outputs a regular homomorphic encryption ciphertext, which can be homomorphically post-processed. As a side result, we describe how to do conjunctive and disjunctive PIR queries. On the other end of the communication--computation spectrum, we take a closer look at Gentry--Ramzan PIR, a scheme with asymptotically optimal communication rate. Here, the bottleneck is the server\u27s computation, which we manage to reduce significantly. Our optimizations enable a tunable trade-off between communication and computation, which allows us to reduce server computation by as much as 85%, at the cost of an increased query size. We further show how to efficiently construct PIR for sparse databases. Our constructions support batched queries, as well as symmetric PIR. We implement all of our PIR constructions, and compare their communication and computation overheads with respect to each other for several application scenarios

Private Intersection-Sum Protocol with Applications to Attributing Aggregate Ad Conversions

In this work, we consider the Intersection-Sum problem: two parties hold datasets containing user identifiers, and the second party additionally has an integer value associated with each user identifier. The parties want to learn the number of users they have in common, and the sum of the associated integer values, but “nothing more”. We present a novel protocol tackling this problem using Diffie-Hellman style Private Set Intersection techniques together with Paillier homomorphic encryption. We prove security of our protocol in the honest-but-curious model. We also discuss applications for the protocol for attributing aggregate ad conversions. Finally, we present a variant of the protocol, which allows aborting if the intersection is too small, in which case neither party learns the intersection-sum