Biblio

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 18.3 x 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.

Focusing on human experience of vulnerability in everyday life interaction scenarios is still a novel approach. So far, only a proof-of-concept online study has been conducted, and to extend this work, we present a follow-up online study. We consider in more detail how human experience of vulnerability caused by a trust violation through a privacy breach affects trust ratings in an interaction scenario with the PEPPER robot assisting with clothes shopping. We report the results from 32 survey responses and 11 semi-structured interviews. Our findings reveal the existence of the privacy paradox also for studying trust in HRI, which is a common observation describing a discrepancy between the stated privacy concerns by people and their behavior to safeguard it. Moreover, we reflect that participants considered only the added value of utility and entertainment when deciding whether or not to interact with the robot again, but not the privacy breach. We conclude that people might tolerate an untrustworthy robot even when they are feeling vulnerable in the everyday life situation of clothes shopping.

Data privacy is a very important problem to address while sharing data among multiple organizations and has become very crucial in the health sectors since multiple organizations such as hospitals are storing data of patients in the form of Electronic Health Records. Stored data is used with other organizations or research analysts to improve the health care of patients. However, the data records contain sensitive information such as age, sex, and date of birth of the patients. Revealing sensitive data can cause a privacy breach of the individuals. This has triggered research that has led to many different privacy preserving techniques being introduced. Thus, we designed a technique that not only encrypts / hides the sensitive information but also sends the data to different organizations securely. To encrypt sensitive data we use different fuzzy logic membership functions. We then use an autoencoder neural network to send the modified data. The output data of the autoencoder can then be used by different organizations for research analysis.

Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.

In recent trends, privacy preservation is the most predominant factor, on big data analytics and cloud computing. Every organization collects personal data from the users actively or passively. Publishing this data for research and other analytics without removing Personally Identifiable Information (PII) will lead to the privacy breach. Existing anonymization techniques are failing to maintain the balance between data privacy and data utility. In order to provide a trade-off between the privacy of the users and data utility, a Mondrian based k-anonymity approach is proposed. To protect the privacy of high-dimensional data Deep Neural Network (DNN) based framework is proposed. The experimental result shows that the proposed approach mitigates the information loss of the data without compromising privacy.

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.