Data security

Data security means protecting digital data, such as those in a database, from destructive forces and from the unwanted actions of unauthorized users, such as a cyberattack or a data breach. Technologies Disk encryption Disk encryption Disk encryption refers to encryption technology that encrypts data on a hard disk drive. Disk encryption typically takes form in either software (see disk encryption software) or hardware (see disk encryption hardware). Disk encryption is often referred to as on-the-fly encryption (OTFE) or transparent encryption. Software versus hardware-based mechanisms for protecting data Software-based security solutions encrypt the data to protect it from theft. However, a malicious program or a hacker could corrupt the data to make it unrecoverable, making the system unusable. Hardware-based security solutions prevent read and write access to data, which provides very strong protection against tampering and unauthorized access

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Computer security, cyber security, digital security or information technology security (IT security) is the protection of computer systems and networks from attacks by malicious actors that may resu

Information security, sometimes shortened to InfoSec, is the practice of protecting information by mitigating information risks. It is part of information risk management. It typically involves preven

Information privacy is the relationship between the collection and dissemination of data, technology, the public expectation of privacy, contextual information norms, and the legal and political issue

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Data security in location-aware applications: an approach based on RBAC

Maria Luisa Damiani

Data security in a mobile context is a critical issue. Over the last few years a new category of location-based services, the Enterprise LBS (ELBS), has emerged focusing on the demands of mobility in organisations. These applications pose challenging requirements, including the need of selective access to ELBS based on the position of mobile users and spatially bounded organisational roles. To deal with these requirements a novel access control system, named GEO-RBAC, has been developed. GEO-RBAC extends the NIST RBAC (Role-Based Access Control) standard with the notions of spatial role, role-dependent position, role schema and role instance. Further, roles become enabled/disabled based on the position of the user. In the paper we present GEO-RBAC, a full-fledged RBAC-based model, consisting, like RBAC, of three distinct components: the Core GEO-RBAC, the Hierarchical GEO-RBAC and the Constrained GEO-RBAC. The paper focuses on the innovative aspects that have been introduced in the model to account for the spatial dimension. Further, a rigorous specification of the model (reference model) is presented.

2007

TEE-based decentralized recommender systems: The raw data sharing redemption

Akash Balasaheb Dhasade, Nevena Dresevic, Anne-Marie Kermarrec, Rafael Pereira Pires

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Federated learning and decentralized learning systems address this by letting the data stay on user's machines to preserve privacy: each user performs the training on local data and only the model parameters are shared. However, sharing the model parameters across the network may still yield privacy breaches. In this paper, we present REX, the first enclave-based decentralized CF recommender. REX exploits Trusted execution environments (TEE), such as Intel software guard extensions (SGX), that provide shielded environments within the processor to improve convergence while preserving privacy. Firstly, REX enables raw data sharing, which ultimately speeds up convergence and reduces the network load. Secondly, REX fully preserves privacy. We analyze the impact of raw data sharing in both deep neural network (DNN) and matrix factorization (MF) recommenders and showcase the benefits of trusted environments in a full-fledged implementation of REX. Our experimental results demonstrate that through raw data sharing, REX significantly decreases the training time by 18.3x and the network load by 2 orders of magnitude over standard decentralized approaches that share only parameters, while fully protecting privacy by leveraging trustworthy hardware enclaves with very little overhead.

2022

Labeled PSI from Fully Homomorphic Encryption with Malicious Security

Hao Chen, Zhicong Huang

Private Set Intersection (PSI) allows two parties, the sender and the receiver, to compute the intersection of their private sets without revealing extra information to each other. We are interested in the unbalanced PSI setting, where (1) the receiver's set is significantly smaller than the sender's, and (2) the receiver (with the smaller set) has a low-power device. Also, in a Labeled PSI setting, the sender holds a label per each item in its set, and the receiver obtains the labels from the items in the intersection. We build upon the unbalanced PSI protocol of Chen, Laine, and Rindal (CCS 2017) in several ways: we add efficient support for arbitrary length items, we construct and implement an unbalanced Labeled PSI protocol with small communication complexity, and also strengthen the security model using Oblivious Pseudo-Random Function (OPRF) in a pre-processing phase. Our protocols outperform previous ones: for an intersection of 220 and 512 size sets of arbitrary length items our protocol has a total online running time of just 1 second (single thread), and a total communication cost of 4 MB. For a larger example, an intersection of 228 and 1024 size sets of arbitrary length items has an online running time of 12 seconds (multi-threaded), with less than 18 MB of total communication.

ASSOC COMPUTING MACHINERY2018

COM-402: Information security and privacy

This course provides an overview of information security and privacy topics. It introduces students to the knowledge and tools they will need to deal with the security/privacy challenges they are likely to encounter in today's Big Data world. The tools are illustrated with relevant applications.

CS-523: Advanced topics on privacy enhancing technologies

This advanced course will provide students with the knowledge to tackle the design of privacy-preserving ICT systems. Students will learn about existing technologies to prect privacy, and how to evaluate the protection they provide.

COM-405: Mobile networks

This course provides a detailed description of the organization and operating principles of mobile and wireless communication networks.

Computer security, cyber security, digital security or information technology security (IT security) is the protection of computer systems and networks from attacks by malicious actors that may resu