A biometric matching process is disclosed. The biometric matching process may be used to obtain access to a resource managed by an access device using only biometric information. In some embodiments, a biometric template is stored in relation to a user device and/or account information, and is obscured. Upon receiving a request for access to a resource from an access device, the system may identify a number of user devices in proximity to the access device. Biometric templates associated with each of those user devices may be compared to a biometric template received from the access device. Upon identifying a match, the system may provide the access device with account information stored in relation to the matched biometric template. The access device may then complete a transaction using the provided account information and grant access to the requested resource.

A secret share value [f.sub.t(x)-f.sub.t(x)] of f.sub.t(x)-f.sub.t(x) is obtained through secure computation using a secret share value [x] of a real number x, and a secret share value [f.sub.t(x)-f.sub.t(x)].sub.r of (f.sub.t(x)-f.sub.t(x)).sub.r obtained by right-shifting f.sub.t(x)-f.sub.t(x) by the predetermined number of bits is obtained through secure computation using the secret share value [f.sub.t(x)-f.sub.t(x)]. Here, [μ] is a secret share value of μ, n is an integer equal to or greater than 1, t=0, . . . , n−1, u=1, . . . , n−1, f.sub.t(x) is a function of the real number x, f.sub.t(x) is an approximation function of the function f.sub.t(x), a secret share value [f.sub.0(x)] of an approximation function f.sub.0(x) is [f.sub.0(x)]=c.sub.o,0+c.sub.0,1[x], a secret share value [f.sub.u(x)] of an approximation function f.sub.u(x) is [f.sub.u(x)]=c.sub.u,0+c.sub.u,1[x]+c.sub.u,2[f.sub.0(x)]+. . . +c.sub.u,u+1[f.sub.u−1(x)], c.sub.t,0 is a public value, and c.sub.t,1, . . . , c.sub.t,n+1 are coefficients.

A public value 2.sup.σ/m is obtained, and secure computation of public value division [x]/(2.sup.σ/m) using a secret share value [x] and the obtained public value 2.sup.σ/m is performed, so that a secret share value [mx].sub.r of a value obtained by right-shifting mx by σ bits is obtained and output. Here, x is a real number, [•] is a secret share value of •, σ is a positive integer that is the number of bits indicating a right shifting amount, and m is a real number.

Systems and methods of performing time-based cryptography verification for a server having a trusted execution environment (TEE), including: sending, by the server, a verifiable delay function (VDF) challenge at the TEE to a computing device, solving, by the computing device, the VDF challenge, and blocking, by the server, access to the TEE when the VDF challenge is not complete from the computing device.

A method includes providing a public encryption key and a seed to a party and receiving a first encrypted data set encrypted using the public encryption key and marked by the party with a first mark based on the seed. The method also includes aggregating the first encrypted data set into an aggregated data set at an aggregator and receiving an indication that a first operation associated with the party has been performed on the aggregated data set. In response to the receiving, updating the first encrypted data set of the aggregated data set by updating the first mark to a second mark according to the first operation, generating a verification encrypted data set according to at least the second mark and at least the corresponding first operation, verifying the aggregated data set by comparing the updated first encrypted data set and the verification encrypted data set.

Techniques for implementing efficient three-party private set intersection (PSI) are provided. In one set of embodiments these techniques make use of an oblivious key-value store (OKVS), which is a cryptographic data structure that encodes a set of key-value pairs ({k.sub.i, v.sub.i}) and exhibits the following properties: (A) if a receiver decodes the OKVS on some input q=k.sub.j, the output will be v.sub.j, and (B) the receiver cannot tell, from the outputs generated by the OKVS, what keys (i.e., k.sub.i's) are encoded. By using an OKVS, the techniques of the present disclosure can achieve three-party PSI in a manner that is more efficient and scalable than existing protocols.

Methods and systems for consensus-based online authentication are provided. An encryption device may be authenticated based on an authentication cryptogram generated by the encryption device. The encryption device may transmit a request for security assessment to one or more support devices. The support devices may individually assess the encryption device, other security devices, and contextual information. The support devices may choose to participate in a multi-party computation with the encryption device based on the security assessments. Support devices that choose to participate may transmit one or more secret shares or partial computations to the encryption device. The encryption device may use the secret shares or partial computations to generate an authentication cryptogram. The authentication cryptogram may be transmitted to a decryption device, which may decrypt the authentication cryptogram, evaluate its contents, and authenticate the encryption device based on its contents.

A function secret sharing (FSS) scheme that facilitates multiple evaluations of a secret function. The FSS scheme includes a function share based on a secret function and at least one key of a key-homomorphic pseudo random function (PRF). At least one key and a function share are provided to each party in the FSS scheme. In turn, each party may generate an output share comprising a function share output evaluated at a function input and a masking component generated based on the at least one key in relation to the key-homomorphic PRF. In turn, the output shares of each participating party may be combined to evaluate the secret function. The FSS scheme facilitates multiple evaluations of the secret function without leaking information regarding the secret function.

A secure inference over Deep Neural Networks (DNNs) using secure two-party computation to perform privacy-preserving machine learning. The secure inference uses a particular type of comparison that can be used as a building block for various layers in the DNN including, for example, ReLU activations and divisions. The comparison securely computes a Boolean share of a bit representing whether input value x is less than input value y, where x is held by a user of the DNN, and where y is held by a provider of the DNN. Each party computing system parses their input into leaf strings of multiple bits. This is much more efficient than if the leaf strings were individual bits. Accordingly, the secure inference described herein is more readily adapted for using in complex DNNs.

A secure maximum value computation apparatus includes an initialization unit 1 that sets X′=X, a pair creation unit 2 that creates, from among the X′, one or more pairs such that no element is included in two or more pairs, a determination unit 3 that determines, through secure computation, a secret value that is a larger value among [[x.sub.i]]and [[x.sub.i]] included in each of the one or more pairs for each of the one or more pairs that are created, a set updating unit 4 that sets, as a new X′, when there is a secret value that is not included in the one or more pairs in the X′, a set including the secret value that is not included in the one or more pairs in the X′ and the secret value determined by the determination unit, a control unit 5 that performs a control to repeat the above-described processing operations until |X′|=1 holds, and a flag determination unit 6 that determines a flag [[z(x.sub.i)]] (i=1, . . . n) such that [[z(x.sub.g)]]=[[1]] holds when [[x.sub.g]] (g∈[1, n]) is a maximum value and [[z(x.sub.i)]]=[[0]] holds when i≠g holds.