Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

National Institute of Standards and Technology における超伝導研究及び生活

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.

Federated Machine Learning: Concept and Applications

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

In a GPRS system, a method of a mobile station acquiring an IP network address. The method comprises the steps of: the mobile station generating a link identifier and sending it to a gateway over a wireless link in a network address request together with a request to check if the link identifier is unique; the gateway receiving the network address request and checking if the link identifier is unique; the gateway responding with a network address request response including either the sending a unique link identifier confirmed as being unique or a different unique link identifier; the gateway sending a network prefix to the mobile station; the mobile station combining the interface identifier and the network prefix to generate the IP network address.

Address acquisition method and apparatus

Toward Linux kernel memory safety

Transparent authentication (TA) schemes are those in which a user is authenticated by a verifier without requiring explicit user interaction. By doing so, those schemes promise high usability and security simultaneously. The majority of TA implementations rely on the received signal strength as an indicator for the proximity of a user device (prover). However, such implicit proximity verification is not secure against an adversary who can relay messages over a larger distance. In this paper, we propose a novel approach for thwarting relay attacks in TA schemes: the prover permits access to authentication credentials only if it can confirm that it is near the verifier. We present STASH, a system for relay-resilient transparent authentication in which the prover does proximity verification by comparing its approach trajectory towards the intended verifier with known authorized reference trajectories. Trajectories are measured using low-cost sensors commonly available on personal devices. We demonstrate the security of STASH against a class of adversaries and its ease-of-use by analyzing empirical data, collected using a STASH prototype. STASH is efficient and can be easily integrated to complement existing TA schemes.

STASH: Securing transparent authentication schemes using prover-side proximity verification

Unsafe websites consist of malicious as well as inappropriate sites, such as those hosting questionable or offensive content. Website reputation systems are intended to help ordinary users steer away from these unsafe sites. However, the process of assigning safety ratings for websites typically involves humans. Consequently it is time consuming, costly and not scalable. This has resulted in two major problems: (i) a significant proportion of the web space remains unrated and (ii) there is an unacceptable time lag before new websites are rated. In this paper, we show that by leveraging structural and content-based properties of websites, we can reliably and efficiently predict their safety ratings, thereby mitigating both problems. We demonstrate the effectiveness of our approach using four datasets of up to 90,000 websites. We use ratings from Web of Trust (WOT), a popular crowdsourced web reputation system, as ground truth. We propose a novel ensemble classification technique that makes opportunistic use of available structural and content properties of web pages to predict their eventual ratings in two dimensions used by WOT: trustworthiness and child safety. Ours is the first classification system to predict such subjective ratings. The same approach works equally well in identifying malicious websites. Across all datasets, our classification achieves average F$_1$-score in the 74–90 % range.

LookAhead: Augmenting Crowdsourced Website Reputation Systems with Predictive Modeling

Consensus mechanisms used by popular distributed ledgers are highly scalable but notoriously inefficient. Byzantine fault tolerance (BFT) protocols are efficient but far less scalable. Speculative BFT protocols such as Zyzzyva and Zyzzyva5 are efficient and scalable but require a trade-off: Zyzzyva requires only $3f + 1$ replicas to tolerate $f$ faults, but even a single slow replica will make Zyzzyva fall back to more expensive non-speculative operation. Zyzzyva5 does not require a non-speculative fallback, but requires $5f + 1$ replicas in order to tolerate $f$ faults. BFT variants using hardware-assisted trusted components can tolerate a greater proportion of faults, but require that every replica have this hardware. 
We present SACZyzzyva, addressing these concerns: resilience to slow replicas and requiring only $3f + 1$ replicas, with only one replica needing an active monotonic counter at any given time. 
We experimentally evaluate our protocols, demonstrating low latency and high scalability. We prove that SACZyzzyva is optimally robust and that trusted components cannot increase fault tolerance unless they are present in greater than two-thirds of replicas.

Nadarajah Asokan

Papers

Address acquisition method and apparatus

Toward Linux kernel memory safety

STASH: Securing transparent authentication schemes using prover-side proximity verification

LookAhead: Augmenting Crowdsourced Website Reputation Systems with Predictive Modeling

Making Speculative BFT Resilient with Trusted Monotonic Counters