Abstract: Various embodiments relate to a fault detection system and method for a digital signature algorithm, including: producing a digital signature of a message using a digital signature algorithm; storing parameters from a last round of the digital signature algorithm; executing the last round of the digital signature algorithm using the stored parameters to produce a check signature; comparing the digital signature to the check signature; and outputting the digital signature when the digital signature is the same as the check signature.

Abstract: Various embodiments relate to a data processing system comprising instructions embodied in a non-transitory computer readable medium, the instructions for a cryptographic operation including matrix multiplication for lattice-based cryptography in a processor, the instructions, including: applying a first function to the rows of a matrix of polynomials to generate first outputs, wherein the first function excludes the identity function; adding an additional row to the matrix of polynomials to produce a modified matrix, wherein each element in the additional row is generated by a second function applied to a column of outputs associated with each element in the additional row; multiplying the modified matrix with a vector of polynomials to produce an output vector of polynomials; applying a verification function to the output vector that produces an indication of whether a fault occurred in the multiplication of the modified matrix with the vector of polynomials; and carrying out a cryptographic operation using

Abstract: Deskinning device for removing skin from a poultry leg product, comprising a first leg end which comprises at least a part of an ankle joint, and a second leg end opposite to the first leg end, the first leg end being arranged at a higher level than the second leg end, wherein the device comprises at least one skin removal assembly which is adapted to carry out a deskinning process, a first skinning roller rotatable about an axis of rotation, wherein a first roller end is arranged at a higher level than a second roller end, a counter skinning member arranged adjacent said first skinning roller, a gap between the first skinning roller and the counter skinning member, wherein the skin removal assembly is adapted to pull the skin of the poultry leg product into the gap and towards the first roller end.

Abstract: Various embodiments relate to a method for multiplying a first and a second polynomial in a ring q [X]/(Xn+1) where q is a positive integer.

Abstract: A combination of machine learning models is provided, according to certain aspects, by a data-aggregation circuit, and a computer server. The data-aggregation circuit is used to assimilate respective sets of output data from at least one of a plurality of circuits to create a new data set, the respective sets of output data being related in that each set of output data is in response to a common data set processed by the machine learning circuitry in the at least one of the plurality of circuits. The computer server uses the new data set to train machine learning operations in at least one of the plurality of circuits.

Abstract: Various embodiments relate to a data processing system comprising instructions embodied in a non-transitory computer readable medium, the instructions for signing messages using a plurality of one-time signing (OTS) keys and a binary-hash-tree structure having a height h and a plurality of nodes configured to provide a public key having, including: generating and storing an authentication path A[d:h?1] for a first 2d signatures corresponding to the first 2d OTS keys of the plurality of OTS keys, where d is the height of a sub-tree associated with first 2d OTS keys; initiating a signature counter; signing a first message using the first OTS key of the plurality of OTS keys; incrementing the signature counter; determining if 2d messages have been signed; signing a second message and incrementing the signature counter when 2d messages have not been signed; and updating authentication path A[d:h?1] for a second 2d signatures corresponding to the second 2d OTS keys of the plurality of OTS keys when 2d messages hav

Abstract: A device and methods are described that comprise at least one host application and a rich execution environment. At least one interface is operably coupled to the REE for communicating with a remote server. A security sub-system comprises a security monitoring and control circuit coupled to the REE and connectable to the remote server via the REE and the at least one interface. The security monitoring and control circuit comprises an analytics circuit configured to detect an anomaly following a compromisation of the device. The security monitoring and control circuit is arranged to treat the REE as an untrusted component and in response to a detection of a compromisation of the REE or a component in the device that is accessible by the REE by the analytics circuit, the security monitoring and control circuit is configured to re-establish a secure connection to the remote server that tunnels through the REE and at least partially removes the compromisation from the device.

Abstract: Various embodiments relate to a method for masked decoding of a polynomial a using an arithmetic sharing a to perform a cryptographic operation in a data processing system using a modulus q, the method for use in a processor of the data processing system, including: subtracting an offset ? from each coefficient of the polynomial a; applying an arithmetic to Boolean (A2B) function on the arithmetic shares of each coefficient ai of the polynomial a to produce Boolean shares âi that encode the same secret value ai; and performing in parallel for all coefficients a shared binary search to determine which of coefficients ai are greater than a threshold t to produce a Boolean sharing value {circumflex over (b)} of the bitstring b where each bit of b decodes a coefficient of the polynomial a.

Abstract: A method is provided for detecting copying of a machine learning model. In the method, the first machine learning model is divided into a plurality of portions. Intermediate outputs from a hidden layer of a selected one of the plurality of portions is compared to corresponding outputs from a second machine learning model to detect the copying. Alternately, a first seal may be generated using the plurality of inputs and the intermediate outputs from nodes of the selected portion. A second seal from a suspected copy that has been generated the same way is compared to the first seal to detect the copying. If the first and second seals are the same, then there is a high likelihood that the suspected copy is an actual copy. By using the method, only the intermediate outputs of the machine learning model outputs have to be disclosed to others, thus protecting the confidentiality of the model.

Abstract: Various embodiments relate to a method for multiplying a first and a second polynomial in a ring q[X]/(Xn+1) to perform a cryptographic operation in a data processing system where q is a positive integer, the method for use in a processor of the data processing system, comprising: receiving the first polynomial and the second polynomial by the processor; mapping the first polynomial into k smaller third polynomials over k smaller rings based upon primitive roots of unity, where k is a positive integer; mapping the second polynomial into k smaller fourth polynomials over the k smaller rings based upon primitive roots of unity; applying an isomorphism to the k third polynomials resulting in k fifth polynomials; applying the isomorphism to the k fourth polynomials resulting in k sixth polynomials; applying a Kronecker substitution on the k fifth polynomials and the k sixth polynomials and perform the multiplication of the k fifth polynomials and the k sixth polynomials to produce a multiplication result; applyin

Abstract: Embodiments address the problem of detecting anomalies in data sets with respect to well-defined normal behavior. Deviations of data collected in real-time are detected using a previously observed distribution of data known to be benign. Embodiments provide techniques to detect varying types of anomalies by creating multiple aggregation layers having varying granularities on top of the lowest level of data collection. This allows detection of fine anomalies that strongly impact single data points, as well as coarse anomalies that detect multiple data points less strongly. Machine learning models are trained and used to compare real-time data sets against behavior of a benign data set in order to detect differences and to flag anomalous behavior.

Abstract: The present invention relates to a bleaching agent comprising: (A) a polymerizable component comprising an aromatic ester compound including two or more allyloxycarbonyl groups, (B) a colouring component comprising particles of a blue pigment (b1), (C) a polymeric dispersing agent to disperse the particles of the colouring component (B) in the polymerizable component (A), the particles of the colouring component (B) having an average size equal to or lower than 400 nm. The present invention also relates to the preparation and use of the above bleaching agent with polymerizable compositions for optical materials.

Abstract: Various embodiments relate to a method for securely comparing a first polynomial represented by a plurality of arithmetic shares and a second compressed polynomial represented by a bitstring where the bits in the bitstring correspond to coefficients of the second polynomial, including: performing a first masked shift of the shares of the coefficients of the first polynomial based upon the start of the interval corresponding to the compressed coefficient of the second polynomial and a modulus value; performing a second masked shift of the shares of the coefficients of the first polynomial based upon the end of the interval corresponding to the compressed coefficient of the second polynomial; bitslicing the most significant bit of the first masked shift of the shares coefficients of the first polynomial; bitslicing the most significant bit of the second masked shift of the shares coefficients of the first polynomial; and combining the first bitsliced bits and the second bitsliced bits using an AND function to p

Abstract: Various embodiments relate to a hardware device configured to compute a plurality of chained hash functions in parallel, including: a processor implementing p hash functions configured to operate on a small input, where p is an integer; a data unit connected to the plurality of hash functions, configured to store the outputs of plurality of hash functions that are then used as the input to a next round of computing the hash function, wherein the processor receives a single instruction and p small data inputs, and wherein each of the p hash functions are used to perform a chained hash function operation on a respective small input of the p small inputs.

Abstract: Various embodiments relate to a method and system for securely comparing a first and second polynomial, including: selecting a first subset of coefficients of the first polynomial and a second subset of corresponding coefficients of the second polynomial, wherein the coefficients of the first polynomial are split into shares and the first and second polynomials have coefficients; subtracting the second subset of coefficients from one of the shares of the first subset of coefficients; reducing the number of elements in the first subset of coefficients to elements by combining groups of / elements together; generating a random number for each of the elements of the reduced subset of coefficients; summing the product of each of the elements of the reduced subset of coefficients with their respective random numbers; summing the shares of the sum of the products; and generating an output indicating that the first polynomial does not equal the second polynomial when the sum does not equal zero.

Abstract: A method and data processing system are provided for determining if a machine learning model has been copied. The machine learning model has a plurality of nodes, the plurality of nodes is organized as a plurality of interconnected layers, and the plurality of interconnected layers includes an input layer and an output layer. The output layer has a predetermined number of output nodes for classifying input samples into a predetermined number of categories, where each output node corresponds to a category. An additional watermarking node is added to the output layer. The model is trained to classify the input data into the predetermined number of categories and into an additional category for the additional node. The additional node may be added to another model to determine if the another model is a copy or clone of the ML model.

Abstract: Various embodiments relate to a method for securely comparing a first polynomial represented by a plurality of arithmetic shares and a second compressed polynomial represented by a bitstring where the bits in the bitstring correspond to coefficients of the second polynomial, including: performing a first masked shift of the shares of the coefficients of the first polynomial based upon the start of the interval corresponding to the compressed coefficient of the second polynomial and a modulus value; performing a second masked shift of the shares of the coefficients of the first polynomial based upon the end of the interval corresponding to the compressed coefficient of the second polynomial; bitslicing the most significant bit of the first masked shift of the shares coefficients of the first polynomial; bitslicing the most significant bit of the second masked shift of the shares coefficients of the first polynomial; and combining the first bitsliced bits and the second bitsliced bits using an AND function to p

Abstract: Various embodiments relate to a method for masked decoding of a polynomial a using an arithmetic sharing a to perform a cryptographic operation in a data processing system using a modulus q, the method for use in a processor of the data processing system, including: subtracting an offset ? from each coefficient of the polynomial a; applying an arithmetic to Boolean (A2B) function on the arithmetic shares of each coefficient ai of the polynomial a to produce Boolean shares âi that encode the same secret value ai; and performing in parallel for all coefficients a shared binary search to determine which of coefficients ai are greater than a threshold t to produce a Boolean sharing value {circumflex over (b)} of the bitstring b where each bit of b decodes a coefficient of the polynomial a.

Abstract: Various embodiments relate to a method for multiplying a first and a second polynomial in the ring [X]/(XN?1) to perform a cryptographic operation in a data processing system, the method for use in a processor of the data processing system, including: receiving the first polynomial and the second polynomial by the processor; mapping the first polynomial into a third polynomial in a first ring and a fourth polynomial in a second ring using a map; mapping the second polynomial into a fifth polynomial in the first ring and a sixth polynomial in the second ring using the map; multiplying the third polynomial in the first ring with the fifth polynomial in the first ring to produce a first multiplication result; multiplying the fourth polynomial in the second ring with the sixth polynomial in the second ring to produce a second multiplication result using Renes multiplication; and combining the first multiplication result and the second multiplication result using the map.

Abstract: Various embodiments relate to a method for multiplying a first and a second polynomial in the ring [X]/(XN?1) to perform a cryptographic operation in a data processing system, the method for use in a processor of the data processing system, including: receiving the first polynomial and the second polynomial by the processor; mapping the first polynomial into a third polynomial in a first ring and a fourth polynomial in a second ring using a map; mapping the second polynomial into a fifth polynomial in the first ring and a sixth polynomial in the second ring using the map; multiplying the third polynomial in the first ring with the fifth polynomial in the first ring to produce a first multiplication result; multiplying the fourth polynomial in the second ring with the sixth polynomial in the second ring to produce a second multiplication result using Renes multiplication; and combining the first multiplication result and the second multiplication result using the map.