The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.

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Internet of things: Vision, applications and research challenges

We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers $1.73 x communication expansion for 210 users and 220-dimensional vectors, and 1.98 x expansion for 214 users and 224-dimensional vectors over sending data in the clear.

https://dl.acm.org/doi/pdf/10.1145/3133956.3133982

Practical Secure Aggregation for Privacy-Preserving Machine Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.

Advances and Open Problems in Federated Learning

Internet of Things (IoT) is playing a more and more important role after its showing up, it covers from traditional equipment to general household objects such as WSNs and RFID. With the great potential of IoT, there come all kinds of challenges. This paper focuses on the security problems among all other challenges. As IoT is built on the basis of the Internet, security problems of the Internet will also show up in IoT. And as IoT contains three layers: perception layer, transportation layer and application layer, this paper will analyze the security problems of each layer separately and try to find new problems and solutions. This paper also analyzes the cross-layer heterogeneous integration issues and security issues in detail and discusses the security issues of IoT as a whole and tries to find solutions to them. In the end, this paper compares security issues between IoT and traditional network, and discusses opening security issues of IoT.

https://csi.dgist.ac.kr/uploads/Seminar/1407_IoT_SSH.pdf

Security of the Internet of Things: perspectives and challenges

A seamless solution transparently addresses the characteristics of nomadic systems, and enables existing network applications to run reliably in mobile environments. The solution extends the enterprise network, letting network managers provide mobile users with easy access to the same applications as stationary users without sacrificing reliability or centralized management. The solution combines advantages of existing wire-line network standards with emerging mobile standards to create a solution that works with existing network applications. A Mobility Management Server coupled to the mobile network maintains the state of each of any number of Mobile End Systems and handles the complex session management required to maintain persistent connections to the network and to other peer processes. If a Mobile End System becomes unreachable, suspends, or changes network address (e.g., due to roaming from one network interconnect to another), the Mobility Management Server maintains the connection to the associated peer task—allowing the Mobile End System to maintain a continuous connection even though it may temporarily lose contact with its network medium. In one example, Mobility Management Server communicates with Mobile End Systems using Remote Procedure Call and Internet Mobility Protocols.

Method and apparatus for providing mobile and other intermittent connectivity in a computing environment

In this document, we present a state of the art of anonymization techniques for classical tabular datasets. This article is geared towards a general public having some knowledge of mathematics and computer science, but with no need for specific knowledge in anonymization. The objective of this document it to explain anonymization concepts in order to be able to sanitize a dataset and compute reindentification risk. The document contains a large number of examples to help understand the calculations. 
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Dans ce document, nous presentons l'etat de l'art des techniques d'anonymisation pour des bases de donnees classiques (i.e. des tables), a destination d'un public technique ayant une formation universitaire de base en mathematiques et informatique, mais non specialiste. L'objectif de ce document est d'expliquer les concepts permettant de realiser une anonymisation de donnees tabulaires, et de calculer les risques de reidentification. Le document est largement compose d'exemples permettant au lecteur de comprendre comment mettre en oeuvre les calculs.

Techniques d'anonymisation tabulaire : concepts et mise en oeuvre

We propose a protocol that can be used between an RFID tag and a reader to exchange a secret without performing any expensive computation. Similarly to the famous blocker tag suggested by Juels, Rivest, and Szydlo, our scheme makes use of special tags that we call noisy tags. Noisy tags are owned by the reader's manager and set out within the reader's field. They are regular RFID tags that generate noise on the public channel between the reader and the queried tag, such that an eavesdropper cannot differentiate the messages sent by the queried tag from the ones sent by the noisy tag. Consequently, she is unable to identify the secret bits that are sent to the reader. Afterwards, the secret shared by the reader and the tag can be used to launch a secure channel in order to protect communications against eavesdroppers. It can also be used to securely refresh a tag's identifier by, for example, xoring the new identifier with the exchanged secret key. Refreshing tags' identifiers improves privacy since it prevents tracking tags.

Noisy tags : A pretty good key exchange protocol for RFID tags

Method for authenticating a user comprising the following steps: 
- in a training phase, presenting a user with a plurality of images called primed images drawn randomly from an image database (6); 
- in an authentication phase, presenting said user with a plurality of degraded versions of at least a subset of said primed images and with a plurality of degraded versions of non primed images extracted from a memory; 
- receiving from said user respective identifications of said presented degraded primed and non-primed images; 
- determining a global score based upon said received identifications; and 
- authenticating said user based upon the determined global score.

Claude Castelluccia

Papers

Techniques d'anonymisation tabulaire : concepts et mise en oeuvre

Noisy tags : A pretty good key exchange protocol for RFID tags

Method for authenticating a user, associated system and computer program