The ubiquity of data, including multi-media data such as images, enables easy mining and analysis of such data. However, such an analysis might involve the use of sensitive data such as medical records (including radiological images) and financial records. Privacy-preserving machine learning is an approach that is aimed at the analysis of such data in such a way that privacy is not compromised. There are various privacy-preserving data analysis approaches such as k-anonymity, l-diversity, t-closeness and Differential Privacy (DP). Currently, DP is a golden standard of privacy-preserving data analysis due to its robustness against background knowledge attacks. In this paper, we report a scheme for privacy-preserving image classification using Support Vector Machine (SVM) and DP. SVM is chosen as a classification algorithm because unlike variants of artificial neural networks, it converges to a global optimum. SVM kernels used are linear and Radial Basis Function (RBF), while ϵ -differential privacy was the DP framework used. The proposed scheme achieved an accuracy of up to 98%. The results obtained underline the utility of using SVM and DP for privacy-preserving image classification.

Quantum key distribution (QKD), another name for quantum cryptography, is the most advanced subfield of quantum information and communication technology (QICT). The first QKD protocol was proposed in 1984, and since then, more protocols have been proposed. It uses quantum mechanics to enable secure exchange of cryptographic keys. In order to have high confidence in the security of the QKD protocols, such protocols must be proven to be secure against any arbitrary attacks. In this chapter, we discuss and demonstrate security proofs for QKD protocols. Security analysis of QKD protocols can be categorised into two techniques, namely infinite-key and finite-key analyses. Finite-key analysis offers more realistic results than the infinite-key one, while infinite-key analysis provides more simplicity. We briefly provide the background of QKD and also define the basic notion of security in QKD protocols. The cryptographic key is shared between Alice and Bob. Since the key is random and unknown to an eavesdropper, Eve, she is unable to learn anything about the message simply by intercepting the ciphertext. This phenomenon is beyond the ability of classical information processing. We then study some tools that are used in the derivation of security proofs for the infinite-and finite-length key limits.

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We present a high-dimensional measurement device-independent (MDI) quantum key distribution (QKD) protocol employing biphotons to encode information. We exploit the biphotons as qutrits to improve the tolerance to error rate. Qutrits have a larger quantum system; hence they carry more bits of classical information and have improved robustness against eavesdropping compared to qubits. Notably, our proposed protocol is independent of measurement devices, thus eliminating the possibility of side-channel attacks. Also, we employ the finite key analysis approach to study the performance of our proposed protocol under realistic conditions where finite resources are used. Furthermore, we simulated the secret key rate for the proposed protocol in terms of the transmission distance for different fixed amounts of signals. The results prove that this protocol achieves a considerable secret key rate for a moderate transmission distance of 90 km by using $$10^{16}$$ 10 16 signals. Moreover, the expected secret key rate was simulated to examine our protocol’s performance at various intrinsic error rate values, $$Q=(0.3\%,0.6\%,1\%)$$ Q = ( 0.3 % , 0.6 % , 1 % ) caused by misalignment and instability due to the optical system. These results show that reasonable key rates are achieved with a minimum data size of about $$10^{14}$$ 10 14 signals which are realizable with the current technology. Thus, implementing MDI-QKD using finite resources while allowing intrinsic errors due to the optical system makes a giant step forward toward realizing practical QKD implementations.