Biblio
Blockchains are emerging technologies that propose new business models and value propositions. Besides their application for cryptocurrency purposes, as distributed ledgers of transactions, they enable new ways to provision trusted information in a distributed fashion. In this paper, we present our product tagging solution designed to help Small & Medium Enterprises (SMEs) protect their brands against counterfeit products and parallel markets, as well as to enhance UX (User Experience) and promote the brand and product.Our solution combines the use of DLT to assure, in a verifiable and permanent way, the trustworthiness and confidentiality of the information associated to the goods and the innovative CP-ABE encryption technique to differentiate accessibility to the product's information.
In the last few years, cryptocurrency mining has become more and more important on the Internet activity and nowadays is even having a noticeable impact on the global economy. This has motivated the emergence of a new malicious activity called cryptojacking, which consists of compromising other machines connected to the Internet and leverage their resources to mine cryptocurrencies. In this context, it is of particular interest for network administrators to detect possible cryptocurrency miners using network resources without permission. Currently, it is possible to detect them using IP address lists from known mining pools, processing information from DNS traffic, or directly performing Deep Packet Inspection (DPI) over all the traffic. However, all these methods are still ineffective to detect miners using unknown mining servers or result too expensive to be deployed in real-world networks with large traffic volume. In this paper, we present a machine learning-based method able to detect cryptocurrency miners using NetFlow/IPFIX network measurements. Our method does not require to inspect the packets' payload; as a result, it achieves cost-efficient miner detection with similar accuracy than DPI-based techniques.
With the development of network services and people's privacy requirements continue to increase. On the basis of providing anonymous user communication, it is necessary to protect the anonymity of the server. At the same time, there are many threatening crime messages in the dark network. However, many scholars lack the ability or expertise to conduct research on dark-net threat intelligence. Therefore, this paper designs a framework based on Hadoop is hidden threat intelligence. The framework uses HDFS as the underlying storage system to build a HBase-based distributed database to store and manage threat intelligence information. According to the heterogeneous type of the forum, the web crawler is used to collect data through the anonymous TOR tool. The framework is used to identify the characteristics of key dark network criminal networks, which is the basis for the later dark network research.
Blockchain is a database technology that provides the integrity and trust of the system can't make arbitrary modifications and deletions by being an append-only distributed ledger. That is, the blockchain is not a modification or deletion but a CRAB (Create-Retrieve-Append-Burn) method in which data can be read and written according to a legitimate user's access right(For example, owner private key). However, this can not delete the created data once, which causes problems such as privacy breach. In this paper, we propose an on-off block-chained Hybrid Blockchain system to separate the data and save the connection history to the blockchain. In addition, the state is changed to the distributed database separately from the ledger record, and the state is changed by generating the arbitrary injection in the XOR form, so that the history of modification / deletion of the Off Blockchain can be efficiently retrieved.
We present a novel, and use case agnostic method of identifying and circumventing private data exposure across distributed and high-dimensional data repositories. Examples of distributed high-dimensional data repositories include medical research and treatment data, where oftentimes more than 300 describing attributes appear. As such, providing strong guarantees of data anonymity in these repositories is a hard constraint in adhering to privacy legislation. Yet, when applied to distributed high-dimensional data, existing anonymisation algorithms incur high levels of information loss and do not guarantee privacy defeating the purpose of anonymisation. In this paper, we address this issue by using Bayesian networks to handle data transformation for anonymisation. By evaluating every attribute combination to determine the privacy exposure risk, the conditional probability linking attribute pairs is computed. Pairs with a high conditional probability expose the risk of deanonymisation similar to quasi-identifiers and can be separated instead of deleted, as in previous algorithms. Attribute separation removes the risk of privacy exposure, and deletion avoidance results in a significant reduction in information loss. In other words, assimilating the conditional probability of outliers directly in the adjacency matrix in a greedy fashion is quick and thwarts de-anonymisation. Since identifying every privacy violating attribute combination is a W[2]-complete problem, we optimise the procedure with a multigrid solver method by evaluating the conditional probabilities between attribute pairs, and aggregating state space explosion of attribute pairs through manifold learning. Finally, incremental processing of new data is achieved through inexpensive, continuous (delta) learning.
As an efficient deletion method, unlinking is widely used in cloud storage. While unlinking is a kind of incomplete deletion, `deleted data' remains on cloud and can be recovered. To make `deleted data' unrecoverable, overwriting is an effective method on cloud. Users lose control over their data on cloud once deleted, so it is difficult for them to confirm overwriting. In face of such a crucial problem, we propose a Provable and Traceable Assured Deletion (PTAD) scheme in cloud storage based on blockchain. PTAD scheme relies on overwriting to achieve assured deletion. We reference the idea of data integrity checking and design algorithms to verify if cloud overwrites original blocks properly as specific patterns. We utilize technique of smart contract in blockchain to automatically execute verification and keep transaction in ledger for tracking. The whole scheme can be divided into three stages-unlinking, overwriting and verification-and we design one specific algorithm for each stage. For evaluation, we implement PTAD scheme on cloud and construct a consortium chain with Hyperledger Fabric. The performance shows that PTAD scheme is effective and feasible.
Crowdsensing, driven by the proliferation of sensor-rich mobile devices, has emerged as a promising data sensing and aggregation paradigm. Despite useful, traditional crowdsensing systems typically rely on a centralized third-party platform for data collection and processing, which leads to concerns like single point of failure and lack of operation transparency. Such centralization hinders the wide adoption of crowdsensing by wary participants. We therefore explore an alternative design space of building crowdsensing systems atop the emerging decentralized blockchain technology. While enjoying the benefits brought by the public blockchain, we endeavor to achieve a consolidated set of desirable security properties with a proper choreography of latest techniques and our customized designs. We allow data providers to safely contribute data to the transparent blockchain with the confidentiality guarantee on individual data and differential privacy on the aggregation result. Meanwhile, we ensure the service correctness of data aggregation and sanitization by delicately employing hardware-assisted transparent enclave. Furthermore, we maintain the robustness of our system against faulty data providers that submit invalid data, with a customized zero-knowledge range proof scheme. The experiment results demonstrate the high efficiency of our designs on both mobile client and SGX-enabled server, as well as reasonable on-chain monetary cost of running our task contract on Ethereum.
Witnessing the increasingly pervasive deployment of security video surveillance systems(VSS), more and more individuals have become concerned with the issues of privacy violations. While the majority of the public have a favorable view of surveillance in terms of crime deterrence, individuals do not accept the invasive monitoring of their private life. To date, however, there is not a lightweight and secure privacy-preserving solution for video surveillance systems. The recent success of blockchain (BC) technologies and their applications in the Internet of Things (IoT) shed a light on this challenging issue. In this paper, we propose a Lightweight, Blockchain-based Privacy protection (Lib-Pri) scheme for surveillance cameras at the edge. It enables the VSS to perform surveillance without compromising the privacy of people captured in the videos. The Lib-Pri system transforms the deployed VSS into a system that functions as a federated blockchain network capable of carrying out integrity checking, blurring keys management, feature sharing, and video access sanctioning. The policy-based enforcement of privacy measures is carried out at the edge devices for real-time video analytics without cluttering the network.
We consider distributed Kalman filter for dynamic state estimation over wireless sensor networks. It is promising but challenging when network is under cyber attacks. Since the information exchange between nodes, the malicious attacks quickly spread across the entire network, which causing large measurement errors and even to the collapse of sensor networks. Aiming at the malicious network attack, a trust-based distributed processing frame is proposed. Which allows neighbor nodes to exchange information, and a series of trusted nodes are found using truth discovery. As a demonstration, distributed Cooperative Localization is considered, and numerical results are provided to evaluate the performance of the proposed approach by considering random, false data injection and replay attacks.
The fundamental aim of digital forensics is to discover, investigate and protect an evidence, increasing cybercrime enforces digital forensics team to have more accurate evidence handling. This makes digital evidence as an important factor to link individual with criminal activity. In this procedure of forensics investigation, maintaining integrity of the evidence plays an important role. A chain of custody refers to a process of recording and preserving details of digital evidence from collection to presenting in court of law. It becomes a necessary objective to ensure that the evidence provided to the court remains original and authentic without tampering. Aim is to transfer these digital evidences securely using encryption techniques.
Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. These operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a tradeoff between recovery threshold and communication load. In this work, we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.
As Blockchain technology become more understood in recent years and its capability to solve enterprise business use cases become evident, technologist have been exploring Blockchain technology to solve use cases that have been daunting industries for years. Unlike existing technologies, one of the key features of blockchain technology is its unparalleled capability to provide, traceability, accountability and immutable records that can be accessed at any point in time. One application area of interest for blockchain is securing heterogenous networks. This paper explores the security challenges in a heterogonous network of IoT devices and whether blockchain can be a viable solution. Using an experimental approach, we explore the possibility of using blockchain technology to secure IoT devices, validate IoT device transactions, and establish a chain of trust to secure an IoT device mesh network, as well as investigate the plausibility of using immutable transactions for forensic analysis.