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

http://planete.inrialpes.fr/~ccastel/PAPERS/rskmahn.pdf

Robust self-keying mobile ad hoc networks

Set-valued dataset contains different types of items/values per individual, for example, visited locations, purchased goods, watched movies, or search queries. As it is relatively easy to re-identify individuals in such datasets, their release poses significant privacy threats. Hence, organizations aiming to share such datasets must adhere to personal data regulations. In order to get rid of these regulations and also to beneit from sharing, these datasets should be anonymized before their release. In this paper, we revisit the problem of anonymizing set-valued data. We argue that anonymization techniques targeting traditional km-anonymity model, which limits the adversarial background knowledge to at most m items per individual, are impractical for large real-world datasets. Hence, we propose a probabilistic relaxation of km-anonymity and present an anonymization technique to achieve it. This relaxation also improves the utility of the anonymized data. We also demonstrate the effectiveness of our scalable anonymization technique on a real-world location dataset consisting of more than 4 million subscribers of a large European telecom operator. We believe that our technique can be very appealing for practitioners willing to share such large datasets.

Probabilistic km-anonymity efficient anonymization of large set-valued datasets

Federated Learning enables entities to collaboratively learn a shared prediction model while keeping their training data locally. It prevents data collection and aggregation and, therefore, mitigates the associated privacy risks. However, it still remains vulnerable to various security attacks where malicious participants aim at degrading the generated model, inserting backdoors, or inferring other participants' training data. This paper presents a new federated learning scheme that provides different trade-offs between robustness, privacy, bandwidth efficiency, and model accuracy. Our scheme uses biased quantization of model updates and hence is bandwidth efficient. It is also robust against state-of-the-art backdoor as well as model degradation attacks even when a large proportion of the participant nodes are malicious. We propose a practical differentially private extension of this scheme which protects the whole dataset of participating entities. We show that this extension performs as efficiently as the non-private but robust scheme, even with stringent privacy requirements but are less robust against model degradation and backdoor attacks. This suggests a possible fundamental trade-off between Differential Privacy and robustness.

Federated Learning in Adversarial Settings

The Hipparcli project [CasSlb] uses the Esterel language [Dio95] for describing protocol building blocks. These blocks cui be configured to form an application specific protocol implementation. Using application specific knowledge should improve the performance of such ai iniplciiiciitation when compared to the use of a standard protocol. However, the C code generated by tlie currently existing Esterel compiler is not very fast, and moreover has a largc code size lCas94aI. This is a problem the compiler shares with many similar tools that gencratc executable code starting from a protocol defined in a formal description technique (FDT). This paper describes our design for an optimizer that we incorporated into the Esterel compiler. After analyzing tlic code generated by the Esterel compiler, we decided that two optiiiiizatioii techniques nierit further investigation:

A Compiler-Based Approach To Protocol Optimization

The rapid evolution of networking and the multiplication of new applications re-emphasizes the importance of the efficient communication supports. Implementations must be able to take maximal advantage of the details of application-specific semantics and of specific networking environments. In other words, the application needs to have more control over data transmission. Such control can be obtained by tailoring the communication facilities (or protocols) to the application characteritics, and by integrating the communication control to the application. Because such a task is too complex to be realized manually, we propose to automate the protocol development process using a formal approach. This report presents our approach to the automated design and implementation of application- specific communication protocols based on information provided by the application. Starting from the formal description of an application, our approach is based on a tool called "Protocol Compiler" that will automatically produce the implementation of a communication protocol tailored to the application. The formalism we use is ESTEREL, a synchronous reactive language dedicated to the description of real-time systems. Protocol description and verification using ESTEREL are described, as well as protocol optimization and implementation principles.

/pdf/tailored-protocol-development-using-esterel-43nxm05u8a.pdf

Claude Castelluccia

Papers

Robust self-keying mobile ad hoc networks

Probabilistic km-anonymity efficient anonymization of large set-valued datasets

Federated Learning in Adversarial Settings

A Compiler-Based Approach To Protocol Optimization

Tailored Protocol Development Using ESTEREL