137 research outputs found
Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming
Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming
DR.SGX: Hardening SGX Enclaves against Cache Attacks with Data Location Randomization
Recent research has demonstrated that Intel's SGX is vulnerable to various
software-based side-channel attacks. In particular, attacks that monitor CPU
caches shared between the victim enclave and untrusted software enable accurate
leakage of secret enclave data. Known defenses assume developer assistance,
require hardware changes, impose high overhead, or prevent only some of the
known attacks. In this paper we propose data location randomization as a novel
defensive approach to address the threat of side-channel attacks. Our main goal
is to break the link between the cache observations by the privileged adversary
and the actual data accesses by the victim. We design and implement a
compiler-based tool called DR.SGX that instruments enclave code such that data
locations are permuted at the granularity of cache lines. We realize the
permutation with the CPU's cryptographic hardware-acceleration units providing
secure randomization. To prevent correlation of repeated memory accesses we
continuously re-randomize all enclave data during execution. Our solution
effectively protects many (but not all) enclaves from cache attacks and
provides a complementary enclave hardening technique that is especially useful
against unpredictable information leakage
ANALYSIS OF THE IMPLEMENTATION OF MVVM ARCHITECTURE PATTERN ON PERFORMANCE OF IOS MOBILE-BASED APPLICATIONS
Performance efficiency is important in mobile application development because mobile devices have limitations in terms of power and resources. Performance efficiency can be improved by applying architecture patterns. In this paper, we use the Model View ViewModel (MVVM) architecture. The application of the architecture is carried out to analyze how practical the application of the MVVM architecture pattern is in increasing performance efficiency in the mobile application. Performance efficiency is measured based on CPU usage, memory usage, and execution Time. The case study shows that the CPU usage and execution Time on MVVM are smaller than Base architecture pattern from the AR Ruler. This is due to the third-party library RxSwift in the MVVM architecture that increases the application's response so that CPU usage and execution time is better than Base architecture pattern. However, the existence of the third-party library RxSwift has a negative impact on memory usage, resulting in higher memory usage than the Base Architecture Pattern. The MVVM pattern is highly recommended for mobile application development to improve performance efficiency
Privacy-preserving Platforms for Computation on Hybrid Clouds
Ph.DDOCTOR OF PHILOSOPH
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