M. Nateghizad
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12 records found
1
SET-OT
A Secure Equality Testing Protocol Based on Oblivious Transfer
A novel approach for data packing
Using trapdoor knapsack
Processing encrypted data is a well-known solution when protecting privacy-sensitive data from untrusted processing units. However, data expansion, as a result of data encryption, makes undesired computational and communicational overheads in the cryptographic applications. Data packing is one of the useful tools to minimize the overheads. In this work, we introduce a novel approach for packing encrypted data based on the subset sum problem. We show that our data packing achieve high performance in reducing the overheads and it is significantly more efficient than existing techniques. Moreover, we show that our approach perfectly matches with secure searching protocols for secure data retrieval.
Adversarial instances are malicious inputs designed to fool machine learning models. In particular, motivated and sophisticated attackers intentionally design adversarial instances to evade classifiers which have been trained to detect security violation, such as malware detection. While the existing approaches provide effective solutions in detecting and defending adversarial samples, they fail to detect them when they are encrypted. In this study, a novel framework is proposed which employs statistical test to detect adversarial instances, when data under analysis are encrypted. An experimental evaluation of our approach shows its practical feasibility in terms of computation cost.
However, handling medical data this way causes concern for privacy. Often the data handled by these devices is very sensitive and could easily be used to identify the user and monitor many of their behaviours. In order to achieve privacy there are several approaches. One way is to enforce involved parties through legislation to use the data for specific purposes only. However, this relies on the party being semi-trusted and does not guarantee safety in case of a data-breach.
In this work the way in which the integration of wearables into the medical domain is currently taking place and how privacy and security is handled will be explored. Furthermore we will show the current state of research regarding improving this security. ...
However, handling medical data this way causes concern for privacy. Often the data handled by these devices is very sensitive and could easily be used to identify the user and monitor many of their behaviours. In order to achieve privacy there are several approaches. One way is to enforce involved parties through legislation to use the data for specific purposes only. However, this relies on the party being semi-trusted and does not guarantee safety in case of a data-breach.
In this work the way in which the integration of wearables into the medical domain is currently taking place and how privacy and security is handled will be explored. Furthermore we will show the current state of research regarding improving this security.
Secure equality testing of two private values is one of the fundamental building blocks of many cryptographic protocols designed for Signal Processing in the Encrypted Domain (SPED). Existing protocols introduce significant amount of computation and computational overhead, which makes it essential to search for new and novel, efficient equality tests for the design of SPED algorithms. In this paper, we first describe the state-of-The-Art equality tests, and then propose two cryptographic protocols which are significantly more efficient than the existing work. Our proposals achieve high performance due to algorithmic changes and successful deployment of data packing. Furthermore, we also present a novel secure exponentiation protocol as a part of our first equality test. Complexity and performance analyses clearly indicate the high efficiency of our protocols in terms of computation cost.
In smart grids, providing power consumption statistics to the customers and generating recommendations for managing electrical devices are considered to be effective methods that can help to reduce energy consumption. Unfortunately, providing power consumption statistics and generating recommendations rely on highly privacy-sensitive smart meter consumption data. From the past experience, we see that it is essential to find scientific solutions that enable the utility providers to provide such services for their customers without damaging customers’ privacy. One effective approach relies on cryptography, where sensitive data is only given in the encrypted form to the utility provider and is processed under encryption without leaking content. The proposed solutions using this approach are very effective for privacy protection but very expensive in terms of computation and communication. In this paper, we focus on an essential operation for designing a privacy-preserving recommender system for smart grids, namely comparison, that takes two encrypted values and outputs which one is greater than the other one. We improve the state-of-the-art comparison protocol based on Homomorphic Encryption in terms of computation and communication by 56 and 25 %, respectively, by introducing algorithmic changes and data packing. As the smart meters are very limited devices, the overall improvement achieved is promising for the future deployment of such cryptographic protocols for enabling privacy enhanced services in smart grids.