By Manas A. Pathak
This thesis discusses the privateness matters in speech-based purposes equivalent to biometric authentication, surveillance, and exterior speech processing prone. writer Manas A. Pathak provides recommendations for privacy-preserving speech processing purposes akin to speaker verification, speaker id and speech reputation. the writer additionally introduces the various instruments from cryptography and computing device studying and present ideas for bettering the potency and scalability of the offered recommendations. Experiments with prototype implementations of the recommendations for execution time and accuracy on standardized speech datasets also are integrated within the textual content. utilizing the framework proposed may perhaps now guarantee that a surveillance employer to hear for a recognized terrorist with no with the ability to listen dialog from non-targeted, blameless civilians.
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Additional resources for Privacy-Preserving Machine Learning for Speech Processing
Example text
I The server only has access to the client’s public key and not the private key. It observes the client’s input vector in ciphertext and cannot decrypt it, but is still able to compute the inner product. 2. BGN Cryptosystem The BGN (Boneh-Goh-Nissim) cryptosystem is designed to satisfy additive homomorphism like the Paillier cryptosystem, but in addition to that, it supports one homomorphic multiplication operation on ciphertext. Being able to perform either addition or multiplication alone, or in other words, being able to evaluate 2-DNF formulas on ciphertexts has its limitations; given two encrypted vectors E[x] = (E[x1 ], .
We choose two random generators g, u of the group G and set h = uq2 , thereby making it the generator of a subgroup of G of order q1 . The public key pk is given by the tuple (n, G, G1 , e, g, h) and the private key sk is the number q1 . For the plaintexts in the set D = {1, . . , T }, we define the encryption function E : D → G as E[x] = gx hr , where r is a randomly chosen number in Zn = {0, . . , n − 1} and the exponentiation is modular in G. Using a different value of r in each execution of the encryption function provides semantic security—two different encryptions of a number x say, E[x; r1 ] and E[x; r2 ] will have different values but 32 3 Privacy Background decrypting each of them will result in the same number x.
In speech-processing applications that we consider in this thesis, the two parties are client and server with the speech input and speech models respectively. The parties are interested in performing tasks such as speaker verification, speaker identification, and speech recognition. This usually involves computing a score of the speech models with respect to the speech input. In this section we use a running example to illustrate SMC protocols: we consider the client speech input to be a d-dimensional vector x represented using MFCC features and a linear classification model w, also a d-dimensional vector, such that the score is given by the inner product wT x.