There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

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

The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

/pdf/a-survey-on-iot-security-application-areas-security-threats-1gb31j78px.pdf

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

The counterfeiting of pharmaceutics or luxury objects is a major threat to supply chains today. As different facilities of a supply chain are distributed and difficult to monitor, malicious adversaries can inject fake objects into the supply chain. This paper presents TRACKER, a protocol for object genuineness verification in RFIDbased supply chains. More precisely, TRACKER allows to securely identify which (legitimate) path an object/tag has taken through a supply chain. TRACKER provides privacy: an adversary can neither learn details about an object’s path, nor can it trace and link objects in the supply chain. TRACKER’s security and privacy is based on an extension of polynomial signature techniques for run-time fault detection using homomorphic encryption. Contrary to related work, RFID tags in this paper are not required to perform any computation, but only feature a few bytes of storage such as ordinary EPC Class 1 Gen 2 tags.

/pdf/tracker-security-and-privacy-for-rfid-based-supply-chains-zdha8ln81g.pdf

Tracker: Security and Privacy for RFID-based Supply Chains.

Counterfeit detection in RFID-based supply chains aims at preventing adversaries from injecting fake products that do not meet quality standards. This paper introduces CHECKER, a new protocol for counterfeit detection in RFID-based supply chains through on-site checking. While RFID-equipped products travel through the supply chain, RFID readers can verify product genuineness by checking the validity of the product's path. CHECKER uses a polynomial-based encoding to represent paths in the supply chain. Each tag T in CHECKER stores an IND-CCA encryption of T's identifier ID and a signature of ID using the polynomial encoding of T's path as secret key. CHECKER is provably secure and privacy preserving. An adversary can neither inject fake products into the supply chain nor trace products. Moreover, RFID tags in CHECKER can be cheap read/write only tags that do not perform any computation. Per tag, only 120 Bytes storage are required.

/pdf/checker-on-site-checking-in-rfid-based-supply-chains-3ej28qteif.pdf

CHECKER: on-site checking in RFID-based supply chains

Cloud Computing raises various security and privacy challenges due to the customers’ inherent lack of control over their outsourced data. One approach to encourage customers to take advantage of the cloud is the design of new accountability solutions which improve the degree of transparency with respect to data processing. In this paper, we focus on accountability policies and propose A-PPL, an accountability policy language that represents machine-readable accountability policies. A-PPL extends the PPL language by allowing customers to define additional rules on data retention, data location, logging and notification. The use of A-PPL is illustrated with a use case where medical sensors collect personal data which are then stored and processed in the cloud. We define accountability obligations related to this use case and translate them into A-PPL policies as a proof of concept of our proposal.

/pdf/a-ppl-an-accountability-policy-language-kwfaoaio15.pdf

A-PPL: An accountability policy language

With the advent of networking applications collecting user data on a massive scale, the privacy of individual users appears to be a major concern. The main challenge is the design of a solution that allows the data analyzer to compute global statistics over the set of individual inputs that are protected by some confidentiality mechanism. Joye et al. [7] recently suggested a solution that allows a centralized party to compute the sum of encrypted inputs collected through a smart metering network. The main shortcomings of this solution are its reliance on a trusted dealer for key distribution and the need for frequent key updates. In this paper we introduce a secure protocol for aggregation of time-series data that is based on the Joye et al. [7] scheme and in which the main shortcomings of the latter, namely, the requirement for key updates and for the trusted dealer are eliminated. Moreover our scheme supports a dynamic group management, whereby as opposed to Joye et al. [7] leave and join operations do not trigger a key update at the users.

/pdf/private-and-dynamic-time-series-data-aggregation-with-trust-2968vela03.pdf

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

With the advent of cloud computing, individuals and companies alike are looking for opportunities to leverage cloud resources not only for storage but also for computation. Nevertheless, the reliance on the cloud to perform computation raises the unavoidable challenge of how to assure the correctness of the delegated computation. In this regard, we introduce two cryptographic protocols for publicly verifiable computation that allow a lightweight client to securely outsource to a cloud server the evaluation of high-degree univariate polynomials and the multiplication of large matrices. Similarly to existing work, our protocols follow the amortized verifiable computation approach. Furthermore, by exploiting the mathematical properties of polynomials and matrices, they are more efficient and give way to public delegatability. Finally, besides their efficiency, our protocols are provably secure under well-studied assumptions.

Kaoutar Elkhiyaoui

Papers

Tracker: Security and Privacy for RFID-based Supply Chains.

CHECKER: on-site checking in RFID-based supply chains

A-PPL: An accountability policy language

Private and Dynamic Time-Series Data Aggregation with Trust Relaxation

Efficient Techniques for Publicly Verifiable Delegation of Computation