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A private set membership (PSM) protocol allows a “receiver” to learn whether its input x is contained in a large database 𝖣𝖡 held by a “sender”. In this work, we define and construct credible private set membership (C-PSM) protocols: in addition to the conventional notions of privacy, C-PSM provides a soundness guarantee that it is hard for a sender (that does not know x) to convince the receiver that 𝑥∈𝖣𝖡. Furthermore, the communication complexity must be logarithmic in the size of 𝖣𝖡. We provide 2-round (i.e., round-optimal) C-PSM constructions based on standard assumptions: We present a black-box construction in the plain model based on DDH or LWE. Next, we consider protocols that support predicates f beyond string equality, i.e., the receiver can learn if there exists 𝑤∈𝖣𝖡 such that 𝑓(𝑥,𝑤)=1. We present two results with transparent setups: (1) A black-box protocol, based on DDH or LWE, for the class of NC1 functions f which are efficiently searchable. (2) An LWE-based construction for all bounded-depth circuits. The only non-black-box use of cryptography in this construction is through the bootstrapping procedure in fully homomorphic encryption. As an application, our protocols can be used to build enhanced round-optimal leaked password notification services, where unlike existing solutions, a dubious sender cannot fool a receiver into changing its password. https://doi.org/10.1007/978-3-031-31371-4_6 

Recent new constructions of rate-1 OT [Döttling, Garg, Ishai, Malavolta, Mour, and Ostrovsky, CRYPTO 2019] have brought this primitive under the spotlight and the techniques have led to new feasibility results for private-information retrieval, and homomorphic encryption for branching programs. The receiver communication of this construction consists of a quadratic (in the sender's input size) number of group elements for a single instance of rate-1 OT. Recently [Garg, Hajiabadi, Ostrovsky, TCC 2020] improved the receiver communication to a linear number of group elements for a single string-OT. However, most applications of rate-1 OT require executing it multiple times, resulting in large communication costs for the receiver. In this work, we introduce a new technique for amortizing the cost of multiple rate-1 OTs. Specifically, based on standard pairing assumptions, we obtain a two-message rate-1 OT protocol for which the amortized cost per string-OT is asymptotically reduced to only four group elements. Our results lead to significant communication improvements in PSI and PIR, special cases of SFE for branching programs. - PIR: We obtain a rate-1 PIR scheme with client communication cost of $O(\lambda\cdot\log N)$ group elements for security parameter $\lambda$ and database size $N$. Notably, after a one-time setup (or one PIR instance), any following PIR instance only requires communication cost $O(\log N)$ number of group elements. - PSI with unbalanced inputs: We apply our techniques to private set intersection with unbalanced set sizes (where the receiver has a smaller set) and achieve receiver communication of $O((m+\lambda) \log N)$ group elements where $m, N$ are the sizes of the receiver and sender sets, respectively. Similarly, after a one-time setup (or one PSI instance), any following PSI instance only requires communication cost $O(m \cdot \log N)$ number of group elements. All previous sublinear-communication non-FHE based PSI protocols for the above unbalanced setting were also based on rate-1 OT, but incurred at least $O(\lambda^2 m \log N)$ group elements. 

Over recent years, devising classification algorithms that are robust to adversarial perturbations hasemerged as a challenging problem. In particular, deep neural nets (DNNs) seem to be susceptible tosmall imperceptible changes over test instances. However, the line of work inprovablerobustness,so far, has been focused oninformation theoreticrobustness, ruling out even theexistenceof anyadversarial examples. In this work, we study whether there is a hope to benefit fromalgorithmicnature of an attacker that searches for adversarial examples, and ask whether there isanylearning taskfor which it is possible to design classifiers that are only robust againstpolynomial-timeadversaries.Indeed, numerous cryptographic tasks (e.g. encryption of long messages) can only be secure againstcomputationally bounded adversaries, and are indeedimpossiblefor computationally unboundedattackers. Thus, it is natural to ask if the same strategy could help robust learning.We show that computational limitation of attackers can indeed be useful in robust learning bydemonstrating the possibility of a classifier for some learning task for which computational andinformation theoretic adversaries of bounded perturbations have very different power. Namely, whilecomputationally unbounded adversaries can attack successfully and find adversarial examples withsmall perturbation, polynomial time adversaries are unable to do so unless they can break standardcryptographic hardness assumptions. Our results, therefore, indicate that perhaps a similar approachto cryptography (relying on computational hardness) holds promise for achieving computationallyrobust machine learning. On the reverse directions, we also show that the existence of such learningtask in which computational robustness beats information theoretic robustness requires computationalhardness by implying (average-case) hardness o fNP.