Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods

A one-time single-bit fault leaks all previous NTRU-HRSS session keys to a chosen-ciphertext attack

This paper presents an efficient attack that, in the standard IND-CCA2 attack model plus a one-time single-bit fault, recovers the NTRU-HRSS session key. This type of fault is expected to occur for many users through natural DRAM bit flips. In a multi-target IND-CCA2 attack model plus a one-time single-bit fault, the attack recovers every NTRU-HRSS session key that was encapsulated to the targeted public key before the fault. Software carrying out the full multi-target attack, using a simulated fault, is provided for verification. This paper also explains how a change in NTRU-HRSS in 2019 enabled this attack

Private Federated Analytics At Scale

Collecting distributed data from millions of individuals for the purpose of analytics is a common scenario – from Apple collecting typed words and emojis to improve its keyboard suggestions, to Google collecting location data to see how busy restaurants and businesses are. This data is often sensitive, and can be overly revealing about the individuals and communities whose data is being analyzed en masse. Differential privacy has become the gold-standard method to give strong individual privacy guarantees while releasing aggregate statistics about sensitive data. However, the process of computing such statistics can itself be a privacy risk. For instance, a simple approach would be to collect all the raw data at a single central entity, which then computes and releases the statistics. This entity then has to be trusted to not abuse the raw data; in practice, it can be difficult to find an entity with the requisite level of trust. In this thesis, we describe a new approach that uses cryptographic techniques to collect data privately and safely, without placing trust in any party. Although the natural candidates, such as secure multiparty computation (MPC) and fully homomorphic encryption (FHE) do not scale to millions of parties on their own, our key insight is that there are ways to refactor computations in such a way that they can be done using simpler techniques that do scale, such as additively homomorphic encryption. Our solution restructures centralized computations into distributed protocols that can be executed efficiently at scale. The systems we design based on this approach can support billions of participants and can handle a variety of real queries from the literature, including machine learning tasks, Pregel-style graph queries, and queries over large categorical data. We automate the distributed refactoring so that analysts can write the query as if the data were centralized without understanding how the rewriting works, and we protect against malicious parties who aim to poison or bias the results

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Efficient and Side-Channel Resistant Implementations of Next-Generation Cryptography

The rapid development of emerging information technologies, such as quantum computing and the Internet of Things (IoT), will have or have already had a huge impact on the world. These technologies can not only improve industrial productivity but they could also bring more convenience to people’s daily lives. However, these techniques have “side effects” in the world of cryptography – they pose new difficulties and challenges from theory to practice. Specifically, when quantum computing capability (i.e., logical qubits) reaches a certain level, Shor’s algorithm will be able to break almost all public-key cryptosystems currently in use. On the other hand, a great number of devices deployed in IoT environments have very constrained computing and storage resources, so the current widely-used cryptographic algorithms may not run efficiently on those devices. A new generation of cryptography has thus emerged, including Post-Quantum Cryptography (PQC), which remains secure under both classical and quantum attacks, and LightWeight Cryptography (LWC), which is tailored for resource-constrained devices. Research on next-generation cryptography is of importance and utmost urgency, and the US National Institute of Standards and Technology in particular has initiated the standardization process for PQC and LWC in 2016 and in 2018 respectively. Since next-generation cryptography is in a premature state and has developed rapidly in recent years, its theoretical security and practical deployment are not very well explored and are in significant need of evaluation. This thesis aims to look into the engineering aspects of next-generation cryptography, i.e., the problems concerning implementation efficiency (e.g., execution time and memory consumption) and security (e.g., countermeasures against timing attacks and power side-channel attacks). In more detail, we first explore efficient software implementation approaches for lattice-based PQC on constrained devices. Then, we study how to speed up isogeny-based PQC on modern high-performance processors especially by using their powerful vector units. Moreover, we research how to design sophisticated yet low-area instruction set extensions to further accelerate software implementations of LWC and long-integer-arithmetic-based PQC. Finally, to address the threats from potential power side-channel attacks, we present a concept of using special leakage-aware instructions to eliminate overwriting leakage for masked software implementations (of next-generation cryptography)

Security and Privacy in Smart Grid

Smart grid utilizes different communication technologies to enhance the reliability and efficiency of the power grid; it allows bi-directional flow of electricity and information, about grid status and customers requirements, among different parties in the grid, i.e., connect generation, distribution, transmission, and consumption subsystems together. Thus, smart grid reduces the power losses and increases the efficiency of electricity generation and distribution. Although smart grid improves the quality of grid's services, it exposes the grid to the cyber security threats that communication networks suffer from in addition to other novel threats because of power grid's nature. For instance, the electricity consumption messages sent from consumers to the utility company via wireless network may be captured, modified, or replayed by adversaries. As a consequent, security and privacy concerns are significant challenges in smart grid. Smart grid upgrade creates three main communication architectures: The first one is the communication between electricity customers and utility companies via various networks; i.e., home area networks (HANs), building area networks (BANs), and neighbour area networks (NANs), we refer to these networks as customer-side networks in our thesis. The second architecture is the communication between EVs and grid to charge/discharge their batteries via vehicle-to-grid (V2G) connection. The last network is the grid's connection with measurements units that spread all over the grid to monitor its status and send periodic reports to the main control center (CC) for state estimation and bad data detection purposes. This thesis addresses the security concerns for the three communication architectures. For customer-side networks, the privacy of consumers is the central concern for these networks; also, the transmitted messages integrity and confidentiality should be guaranteed. While the main security concerns for V2G networks are the privacy of vehicle's owners besides the authenticity of participated parties. In the grid's connection with measurements units, integrity attacks, such as false data injection (FDI) attacks, target the measurements' integrity and consequently mislead the main CC to make the wrong decisions for the grid. The thesis presents two solutions for the security problems in the first architecture; i.e., the customer-side networks. The first proposed solution is security and privacy-preserving scheme in BAN, which is a cluster of HANs. The proposed scheme is based on forecasting the future electricity demand for the whole BAN cluster. Thus, BAN connects to the electricity provider only if the total demand of the cluster is changed. The proposed scheme employs the lattice-based public key NTRU crypto-system to guarantee the confidentiality and authenticity of the exchanged messages and to further reduce the computation and communication load. The security analysis shows that our proposed scheme can achieve the privacy and security requirements. In addition, it efficiently reduces the communication and computation overhead. According to the second solution, it is lightweight privacy-preserving aggregation scheme that permits the smart household appliances to aggregate their readings without involving the connected smart meter. The scheme deploys a lightweight lattice-based homomorphic crypto-system that depends on simple addition and multiplication operations. Therefore, the proposed scheme guarantees the customers' privacy and message integrity with lightweight overhead. In addition, the thesis proposes lightweight secure and privacy-preserving V2G connection scheme, in which the power grid assures the confidentiality and integrity of exchanged information during (dis)charging electricity sessions and overcomes EVs' authentication problem. The proposed scheme guarantees the financial profits of the grid and prevents EVs from acting maliciously. Meanwhile, EVs preserve their private information by generating their own pseudonym identities. In addition, the scheme keeps the accountability for the electricity-exchange trade. Furthermore, the proposed scheme provides these security requirements by lightweight overhead; as it diminishes the number of exchanged messages during (dis)charging sessions. Simulation results demonstrate that the proposed scheme significantly reduces the total communication and computation load for V2G connection especially for EVs. FDI attack, which is one of the severe attacks that threatens the smart grid's efficiency and reliability, inserts fake measurements among the correct ones to mislead CC to make wrong decisions and consequently impact on the grid's performance. In the thesis, we have proposed an FDI attack prevention technique that protects the integrity and availability of the measurements at measurement units and during their transmission to the CC, even with the existence of compromised units. The proposed scheme alleviates the negative impacts of FDI attack on grid's performance. Security analysis and performance evaluation show that our scheme guarantees the integrity and availability of the measurements with lightweight overhead, especially on the restricted-capabilities measurement units. The proposed schemes are promising solutions for the security and privacy problems of the three main communication networks in smart grid. The novelty of these proposed schemes does not only because they are robust and efficient security solutions, but also due to their lightweight communication and computation overhead, which qualify them to be applicable on limited-capability devices in the grid. So, this work is considered important progress toward more reliable and authentic smart grid

Extending the Exposure Score of Web Browsers by Incorporating CVSS

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Yet its content differs from one browser to another. Despite the privacy and security risks of User-Agent strings, very few works have tackled this problem. Our previous work proposed giving Internet browsers exposure relative scores to aid users to choose less intrusive ones. Thus, the objective of this work is to extend our previous work through: first, conducting a user study to identify its limitations. Second, extending the exposure score via incorporating data from the NVD. Third, providing a full implementation, instead of a limited prototype. The proposed system: assigns scores to users’ browsers upon visiting our website. It also suggests alternative safe browsers, and finally it allows updating the back-end database with a click of a button. We applied our method to a data set of more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available here [4].</p

Actas de las VI Jornadas Nacionales (JNIC2021 LIVE)

Estas jornadas se han convertido en un foro de encuentro de los actores más relevantes en el ámbito de la ciberseguridad en España. En ellas, no sólo se presentan algunos de los trabajos científicos punteros en las diversas áreas de ciberseguridad, sino que se presta especial atención a la formación e innovación educativa en materia de ciberseguridad, y también a la conexión con la industria, a través de propuestas de transferencia de tecnología. Tanto es así que, este año se presentan en el Programa de Transferencia algunas modificaciones sobre su funcionamiento y desarrollo que han sido diseñadas con la intención de mejorarlo y hacerlo más valioso para toda la comunidad investigadora en ciberseguridad