The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Presentacio sobre l'Oficina de Proteccio de Dades Personals de la UAB i la politica Open Science. Va formar part de la conferencia "Les politiques d'Open Data / Open Acces: Implicacions a la recerca" orientada a investigadors i gestors de projectes europeus que va tenir lloc el 20 de setembre de 2018 a la Universitat Autonoma de Barcelona

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General data protection regulation

Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence (especially deep learning, DL) based applications and services are thriving. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. Therefore, edge intelligence , aiming to facilitate the deployment of DL services by edge computing, has received significant attention. In addition, DL, as the representative technique of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. With regard to mutually beneficial edge intelligence and intelligent edge , this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge , i.e., Edge DL.

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

Computer communications

VoLTE (Voice-over-LTE) is the designated voice solution to the LTE mobile network, and its worldwide deployment is underway. It reshapes call services from the traditional circuit-switched telecom telephony to the packet-switched Internet VoIP. In this work, we conduct the first study on VoLTE security before its full rollout. We discover several vulnerabilities in both its control-plane and data-plane functions, which can be exploited to disrupt both data and voice in operational networks. In particular, we find that the adversary can easily gain free data access, shut down continuing data access, or subdue an ongoing call, etc. We validate these proof-of-concept attacks using commodity smartphones (rooted and unrooted) in two Tier-1 US mobile carriers. Our analysis reveals that, the problems stem from both the device and the network. The device OS and chipset fail to prohibit non-VoLTE apps from accessing and injecting packets into VoLTE control and data planes. The network infrastructure also lacks proper access control and runtime check.

Insecurity of Voice Solution VoLTE in LTE Mobile Networks

A mobile communication apparatus comprises an anti-theft control circuit and a transmission control circuit. The anti-theft control circuit has an information management module and a security module. The information management module is used for storing a user personal database comprising at least one of a phonebook database, a schedule database, and an e-mail database. The transmission control circuit is used for transmitting data and/or signals. When the transmission control circuit receives a remote message, the security module examines whether the remote message is a predetermined legitimate remote control message; wherein if YES, the transmission control circuit sends out at least one record of the phonebook database, the schedule database, and the e-mail database to an electronic apparatus indicated by the remote message.

Mobile communication apparatus having anti-theft and auto-notification functions

Home Digital Voice Assistants (HDVAs) are getting popular in recent years. Users can control smart devices and get living assistance through those HDVAs (e.g., Amazon Alexa, Google Home) using voice. In this work, we study the insecurity of HDVA service by using Amazon Alexa as a case study. We disclose three security vulnerabilities which root in the insecure access control of Alexa services. We then exploit them to devise two proof-of-concept attacks, home burglary and fake order, where the adversary can remotely command the victim's Alexa device to open a door or place an order from Amazon.com. The insecure access control is that the Alexa device not only relies on a single-factor authentication but also takes voice commands even if no people are around. We thus argue that HDVAs should have another authentication factor, a physical presence based access control; that is, they can accept voice commands only when any person is detected nearby. To this end, we devise a Virtual Security Button (VSButton), which leverages the WiFi technology to detect indoor human motions. Once any indoor human motion is detected, the Alexa device is enabled to accept voice commands. Our evaluation results show that it can effectively differentiate indoor motions from the cases of no motion and outdoor motions in both the laboratory and real world settings.

The Insecurity of Home Digital Voice Assistants - Amazon Alexa as a Case Study.

Home Digital Voice Assistants (HDVAs) are getting popular in recent years. Users can control smart devices and get living assistance through those HDVAs (e.g., Amazon Alexa, Google Home) using voice. In this work, we study the insecurity of HDVA services by using Amazon Alexa and Google Home as case studies. We disclose three security vulnerabilities which root in their insecure access control. We then exploit them to devise two proof-of-concept attacks, home burglary and fake order, where the adversary can remotely command the victim’s HDVA device to open a door or place an order from Amazon.com or Google Express. The insecure access control is that HDVA devices not only rely on a single-factor authentication but also take voice commands even if no people are around them. We thus argue that HDVAs should have another authentication factor, a physical presence based access control; that is, they can accept voice commands only when any person is detected nearby. To this end, we devise a Virtual Security Button (VSButton), which leverages the WiFi technology to detect indoor human motions. Once any indoor human motion is detected, the HDVA device is enabled to accept voice commands. Our evaluation results show that it can effectively differentiate indoor motions from the cases of no motion and outdoor motions in both laboratory and real world settings.

The Insecurity of Home Digital Voice Assistants - Vulnerabilities, Attacks and Countermeasures

An address resolution protocol (ARP) cache management method. An ARP cache comprises a plurality of ARP tables. Each ARP table comprises a plurality of updatable entries. The method comprises: receiving an ARP message; looking up the pluralities of ARP tables to find a message-matching entry; choosing an ARP table for storing new entries; creating a new entry to overwrite an existing entry in the chosen ARP table if no message-matching entry is found after looking up the ARP tables.

Guan-Hua Tu

Papers

Insecurity of Voice Solution VoLTE in LTE Mobile Networks

Mobile communication apparatus having anti-theft and auto-notification functions

The Insecurity of Home Digital Voice Assistants - Amazon Alexa as a Case Study.

The Insecurity of Home Digital Voice Assistants - Vulnerabilities, Attacks and Countermeasures

Address resolution protocol (arp) cache management methods and devices