RECENT PROGRESS OF DIFFERENTIALLY PRIVATE FEDERATED LEARNING WITH THE SHUFFLE MODEL

The emerging of shuffle model has attracted considerable attention of scientists owing to his unique properties in solving the privacy problems in federated learning, specifically the trade off problem between privacy and utility in central and local model. Where, the central model relies on a trusted server which collects users’ raw data and then perturbs it. While in the local model all users perturb their data locally then they send their perturbed data to server. Both models have pron and con. The server in central model enjoys with high accuracy but the users suffer from insufficient privacy in contrast, the local model which provides sufficient privacy at users’ side but the server suffers from limited accuracy. Shuffle model has advanced property of hide position of input messages by perturbing it with perturbation π. Therefore, the scientists considered on adding shuffle model between users and servers to make the server untrusted where the users communicate with the server through the shuffle and boosting the privacy by adding perturbation π for users’ messages without increasing the noise level. Consequently, the usage of modified technique differential privacy federated learning with shuffle model will explores the gap between privacy and accuracy in both models. So this new model attracted many researchers in recent work. In this review, we initiate the analytic learning of a shuffled model for distributed differentially private mechanisms. We focused on the role of shuffle model for solving the problem between privacy and accuracy by summarizing the recent researches about shuffle model and its practical results. Furthermore, we present two types of shuffle, single shuffle and m shuffles with the statistical analysis for each one in boosting the privacy amplification of users with the same level of accuracy by reasoning the practical results of recent papers.

Higher-rate relativistic quantum key distribution

One of the major problems in the field of quantum key distribution (QKD) is the low key rates at which the systems operate. The reasons for this are the processes used to ensure the key distribution itself: sifting, parameter estimation, key reconciliation, and privacy amplification. So, this reduction in the rate of communication is inherent to all existing quantum key distribution schemes. This paper is concerned with proposing a solution to mitigate the rate reduction of the so-called relativistic QKD. To mitigate the reduction, we introduce a modified relativistic QKD protocol, which is based on Mach–Zehnder interferometer being used as a probabilistic basis selection system (basis misalignment occurs between the parties in approximately half of the transferred qubits). The interferometric scheme allows the participating parties to correlate the mutual unbiased bases (MUBs) chosen by them. In this regard, a qubit could be used to transfer more than one bit of information. To be precise, by implementing the proposed interferometric scheme into a relativistic QKD protocol, a qubit is able to transfer two bits of information. This results in achieving a protocol, which is characterized with a greater rate of communication, two times greater than the usual rate. The modified protocol is proven to be secure against intercept-resend and collective attacks.

Stream privacy amplification for quantum cryptography

Privacy amplification is the key step to guarantee the security of quantum communication. The existing security proofs require accumulating a large number of raw key bits for privacy amplification. This is similar to block ciphers in classical cryptography that would delay the final key generation since an entire block must be accumulated before privacy amplification. Moreover, any leftover errors after information reconciliation would corrupt the entire block. By modifying the security proof based on quantum error correction, we develop a stream privacy amplification scheme, which resembles the classical stream cipher, to solve the problems of final key generation delay and error spread. The stream scheme can also help to enhance the security of trusted-relay quantum networks. Inspired by the connection between stream ciphers and quantum error correction in our security analysis, we further develop a generic information-theoretic tool to study the security of classical encryption algorithms.

Hiding Numerical Vectors in Local Private and Shuffled Messages

Numerical vector aggregation has numerous applications in privacy-sensitive scenarios, such as distributed gradient estimation in federated learning, and statistical analysis on key-value data. Within the framework of local differential privacy, this work gives tight minimax error bounds of O(d s/(n epsilon^2)), where d is the dimension of the numerical vector and s is the number of non-zero entries. An attainable mechanism is then designed to improve from existing approaches suffering error rate of O(d^2/(n epsilon^2)) or O(d s^2/(n epsilon^2)). To break the error barrier in the local privacy, this work further consider privacy amplification in the shuffle model with anonymous channels, and shows the mechanism satisfies centralized (14 ln(2/delta) (s e^epsilon+2s-1)/(n-1))^0.5, delta)-differential privacy, which is domain independent and thus scales to federated learning of large models. We experimentally validate and compare it with existing approaches, and demonstrate its significant error reduction.

An Adaptive Lossly Quantization Technique for Key Extraction Applied in Vehicular Communication

Security in Vehicular Ad Hoc Network (VANET) is one of the major challenging topics and the secure key interchange between two legitimate vehicles is an important issue. The multi-environment of VANET has been exploited to extract the secret key and employed security services in VANET. However, it offered more excellence randomness owed to fading, noise multi-path, and velocity difference. Some of the factors like Bit-rate, complication and memory requests are reduced by using a process known as quantization. This paper proposes a new quantization method to extract the secret key for vehicular communications that uses a lossy quantizer in combination with information reconciliation and privacy amplification. Our work focuses on the quantization phase for the secret generation procedure. The comprehensive simulations display the propose method increases the zone and number of the quantization levels to utilize the maximum number of measurements to reduce reasonably the wasted measurements.

Research Challenges in Designing Differentially Private Text Generation Mechanisms

Accurately learning from user data while ensuring quantifiable privacy guarantees provides an opportunity to build better ML models while maintaining user trust. Recent literature has demonstrated the applicability of a generalized form of Differential Privacy to provide guarantees over text queries. Such mechanisms add privacy preserving noise to vectorial representations of text in high dimension and return a text based projection of the noisy vectors. However, these mechanisms are sub-optimal in their trade-off between privacy and utility. In this proposal paper, we describe some challenges in balancing this trade-off. At a high level, we provide two proposals: (1) a framework called LAC which defers some of the noise to a privacy amplification step and (2), an additional suite of three different techniques for calibrating the noise based on the local region around a word. Our objective in this paper is not to evaluate a single solution but to further the conversation on these challenges and chart pathways for building better mechanisms.

Everlasting security of quantum key distribution with 1K-DWCDM and quadratic hash

Quantum key distribution (QKD) offers a very strong property called everlasting security, which says if authentication is unbroken during the execution of QKD, the generated key remains information-theoretically secure indefinitely. For this purpose, we propose the use of certain universal hashing based MACs for use in QKD, which are fast, very efficient with key material, and are shown to be highly secure. Universal hash functions are ubiquitous in computer science with many applications ranging from quantum key distribution and information security to data structures and parallel computing. In QKD, they are used at least for authentication, error correction, and privacy amplification. Using results from Cohen [Duke Math. J., 1954], we also construct some new families of $\varepsilon$-almost-$\Delta$-universal hash function families which have much better collision bounds than the well-known Polynomial Hash. Then we propose a general method for converting any such family to an $\varepsilon$-almost-strongly universal hash function family, which makes them useful in a wide range of applications, including authentication in QKD.