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

The Cluster Exposure Verification (Cléa) Protocol: Specifications of the Lightweight Version

IPv6 Network Ingress Filtering

We highlight privacy risks associated with the HTML5 Battery Status API. We put special focus on its implementation in the Firefox browser. Our study shows that websites can discover the capacity of users’ batteries by exploiting the high precision readouts provided by Firefox on Linux. The capacity of the battery, as well as its level, expose a fingerprintable surface that can be used to track web users in short time intervals. Our analysis shows that the risk is much higher for old or used batteries with reduced capacities, as the battery capacity may potentially serve as a tracking identifier. The fingerprintable surface of the API could be drastically reduced without any loss in the API’s functionality by reducing the precision of the readings. We propose minor modifications to Battery Status API and its implementation in the Firefox browser to address the privacy issues presented in the study. Our bug report for Firefox was accepted and a fix is deployed.

/pdf/the-leaking-battery-a-privacy-analysis-of-the-html5-battery-1ubfp3s3fe.pdf

The leaking battery: A privacy analysis of the HTML5 Battery Status API.

In order to protect individuals' privacy, governments and institutions impose some obligations on data sharing and publishing. Mainly, they require the data to be " anonymized ". In this paper, we shortly discuss the criteria introduced by European General Data Protection Regulation to assess anonymized data. We argue that the evaluation of anonymized data should be based on whether the data allows individual based inferences, instead of being centered around the concept of re-identification as the regulation has proposed. We then propose a framework that allows us to evaluate a given (anonymized) dataset. Finally, we apply our framework to evaluate two real datasets after being anonymized using k-anonymity and l-diversity techniques.

https://hal.inria.fr/hal-01681014/document

Differential Inference Testing A Practical Approach to Evaluate Anonymized Data

We propose a hash-based paging and location update procedure that reduces the paging cost in cellular systems. By applying a Bloom filter, the terminal identifier field of a paging message is coded to page a number of terminals concurrently.

https://hal.inria.fr/inria-00466582/document

Claude Castelluccia

Papers

The Cluster Exposure Verification (Cléa) Protocol: Specifications of the Lightweight Version

IPv6 Network Ingress Filtering

The leaking battery: A privacy analysis of the HTML5 Battery Status API.

Differential Inference Testing A Practical Approach to Evaluate Anonymized Data

A hash-based paging and location update procedure