International Association for Cryptologic Research

International Association
for Cryptologic Research

CryptoDB

Noam Mazor

Publications

Year
Venue
Title
2020
TCC
Lower Bounds on the Time/Memory Tradeoff of Function Inversion 📺
We study time/memory tradeoffs of function inversion: an algorithm, i.e., an inverter, equipped with an s-bit advice on a randomly chosen function f:[n]->[n] and using q oracle queries to f, tries to invert a randomly chosen output y of f (i.e., to find x such that f(x)=y). Much progress was done regarding adaptive function inversion - the inverter is allowed to make adaptive oracle queries. Hellman [IEEE transactions on Information Theory '80] presented an adaptive inverter that inverts with high probability a random f. Fiat and Naor [SICOMP '00] proved that for any s,q with s^3 q = n^3 (ignoring low-order terms), an s-advice, q-query variant of Hellman's algorithm inverts a constant fraction of the image points of any function. Yao [STOC '90] proved a lower bound of sq<=n for this problem. Closing the gap between the above lower and upper bounds is a long-standing open question. Very little is known of the non-adaptive variant of the question - the inverter chooses its queries in advance. The only known upper bounds, i.e., inverters, are the trivial ones (with s+q=n), and the only lower bound is the above bound of Yao. In a recent work, Corrigan-Gibbs and Kogan [TCC '19] partially justified the difficulty of finding lower bounds on non-adaptive inverters, showing that a lower bound on the time/memory tradeoff of non-adaptive inverters implies a lower bound on low-depth Boolean circuits. Bounds that, for a strong enough choice of parameters, are notoriously hard to prove. We make progress on the above intriguing question, both for the adaptive and the non-adaptive case, proving the following lower bounds on restricted families of inverters: Linear-advice (adaptive inverter): If the advice string is a linear function of f (e.g., A*f, for some matrix A, viewing f as a vector in [n]^n), then s+q is \Omega(n). The bound generalizes to the case where the advice string of f_1 + f_2, i.e., the coordinate-wise addition of the truth tables of f_1 and f_2, can be computed from the description of f_1 and f_2 by a low communication protocol. Affine non-adaptive decoders: If the non-adaptive inverter has an affine decoder - it outputs a linear function, determined by the advice string and the element to invert, of the query answers - then s is \Omega(n) (regardless of q). Affine non-adaptive decision trees: If the non-adaptive inverter is a d-depth affine decision tree - it outputs the evaluation of a decision tree whose nodes compute a linear function of the answers to the queries - and q < cn for some universal c>0, then s is \Omega(n/d \log n).
2019
TCC
Channels of Small Log-Ratio Leakage and Characterization of Two-Party Differentially Private Computation
Consider a ppt two-party protocol $$\varPi = (\mathsf {A} ,\mathsf {B} )$$ in which the parties get no private inputs and obtain outputs $$O^{\mathsf {A} },O^{\mathsf {B} }\in \left\{ 0,1\right\} $$, and let $$V^\mathsf {A} $$ and $$V^\mathsf {B} $$ denote the parties’ individual views. Protocol $$\varPi $$ has $$\alpha $$-agreement if $$\Pr [O^{\mathsf {A} }=O^{\mathsf {B} }] = \tfrac{1}{2}+\alpha $$. The leakage of $$\varPi $$ is the amount of information a party obtains about the event $$\left\{ O^{\mathsf {A} }=O^{\mathsf {B} }\right\} $$; that is, the leakage$$\epsilon $$ is the maximum, over $$\mathsf {P} \in \left\{ \mathsf {A} ,\mathsf {B} \right\} $$, of the distance between $$V^\mathsf {P} |_{O^{\mathsf {A} }= O^{\mathsf {B} }}$$ and $$V^\mathsf {P} |_{O^{\mathsf {A} }\ne O^{\mathsf {B} }}$$. Typically, this distance is measured in statistical distance, or, in the computational setting, in computational indistinguishability. For this choice, Wullschleger [TCC ’09] showed that if $$\epsilon \ll \alpha $$ then the protocol can be transformed into an OT protocol.We consider measuring the protocol leakage by the log-ratio distance (which was popularized by its use in the differential privacy framework). The log-ratio distance between X, Y over domain $$\varOmega $$ is the minimal $$\epsilon \ge 0$$ for which, for every $$v \in \varOmega $$, $$\log \frac{\Pr [X=v]}{\Pr [Y=v]} \in [-\epsilon ,\epsilon ]$$. In the computational setting, we use computational indistinguishability from having log-ratio distance $$\epsilon $$. We show that a protocol with (noticeable) accuracy $$\alpha \in \varOmega (\epsilon ^2)$$ can be transformed into an OT protocol (note that this allows $$\epsilon \gg \alpha $$). We complete the picture, in this respect, showing that a protocol with $$\alpha \in o(\epsilon ^2)$$ does not necessarily imply OT. Our results hold for both the information theoretic and the computational settings, and can be viewed as a “fine grained” approach to “weak OT amplification”.We then use the above result to fully characterize the complexity of differentially private two-party computation for the XOR function, answering the open question put by Goyal, Khurana, Mironov, Pandey, and Sahai, [ICALP ’16] and Haitner, Nissim, Omri, Shaltiel, and Silbak [22] [FOCS ’18]. Specifically, we show that for any (noticeable) $$\alpha \in \varOmega (\epsilon ^2)$$, a two-party protocol that computes the XOR function with $$\alpha $$-accuracy and $$\epsilon $$-differential privacy can be transformed into an OT protocol. This improves upon Goyal et al. that only handle $$\alpha \in \varOmega (\epsilon )$$, and upon Haitner et al. who showed that such a protocol implies (infinitely-often) key agreement (and not OT). Our characterization is tight since OT does not follow from protocols in which $$\alpha \in o( \epsilon ^2)$$, and extends to functions (over many bits) that “contain” an “embedded copy” of the XOR function.