Privacy Clouds

PARALLEL AND DISTRIBUTED SYSTEMS, PRIVACY ON THE CLOUDS 1 Privacy on the Clouds Andriy Mazayev, Filipe J. E. Reis, Florentino R. Bexiga, Luís F. C. Jacinto Abstract— Privacy is a very critical subject. People like to have their belongings kept private and be able to sometimes hide it from others. Some computer systems require users to trust those “belongings” to someone. Cloud computing based systems are such systems. In these cases, privacy is much more difficult to control. Users are deliberately sending their private files to some place unknown, and they are not sure who can see them, not only that, sometimes their files or information has to be shared between systems and/or organizations. If a government agency asks for access to a user’s private files, the server owner has to obey, and the user might never know what happened. Cloud providers need to have appropriate mechanisms to prevent that user files are used in a way that hasn’t been agreed upon. This is just an example a privacy issue that can happen when dealing with cloud computing based systems. This paper starts with a history introduction before reaching our main topic: cloud computing, more precisely, privacy in cloud computing. The paper will address key aspects about privacy as well as ways to insure that privacy. Index Terms—Distributed systems, Cloud Computing, Privacy, Security. —————————— —————————— 1 INTRODUCTION OMPUTER systems are constantly evolving. In the beginning, computers were very large and extremely expensive. As a result, many organizations had only one or a few machines to work with, and due to the lack of a way to connect each other, they had to work inde- pendently. [1] At the time, computers could already perform three- hundred operations per second, which was a thousand times faster than any calculator, but even so, there was a continuous search for more computing power. [2] This search led to the development of powerful micropro- cessors. A lot of them had the computational power of a mainframe, but cost a lot less. This allowed organizations to get more machines for their needs by a smaller price, as well as made possible for home users who needed or just wanted a computer to get one. We went from machines that cost ten million euros and could only execute one instruction per second, to machines that cost a thousand euros and can execute a billion instructions per second. [1] This evolution in computation was achieved by making the clocks run faster and computer parts smaller, thus increasing processing speed, but unfortunately there are physical limits which cannot be ignored. According to Einstein’s special theory of relativity, no electrical signal can propagate itself faster than the speed of light. Not only that, but the faster the clock runs, the more heat it produces, and as the size of the computer decreases the difficulty to dissipate the heat increases, making it harder to cool off. [2] A F1 automobiles can be fast, but it’s no good if they blow before reaching the finishing line. One way of getting more processing power, without hit- ting those limits is by using parallel and distributed systems. These systems were very different from what we had before with centralized systems, or single processor systems, which were composed by only one computer, its peripherals and maybe some remote terminals. [2] Parallel Systems are systems where there are various Central Processing Units (CPUs) working in parallel and they can be classified as multiprocessors or multicomputers. A multiprocessor system is a system in which multi- ple processing units share full access to a common Ran- dom Access Memory (RAM); therefore they all share the same virtual address space. Multicomputers (also known as clusters) on the other hand, are systems in which mul- tiple processing units are grouped but do not share memory as multiprocessor systems, they share data through a high-speed local-area network (LAN). They can share the same disk space as well. These types of systems are more commonly used to perform big amounts of cal- culus and simulations for weather predictions or econo- my. [2] A Distributed System is “collection of independent computers that appear to its users as a single coherent system” [1]. On other words, a user accesses a system that appears singular, but that in reality can have lots of servers working simultaneously. A defining factor in distributed systems is location. In a distributed system, the various computers do not have to share the same location, therefore being connected via internet instead of a high- speed local-area network (LAN). A user can send files to a web service which may have servers working in China and Carolina at the same time but the user has no knowledge of this fact since the systems appears single and coherent to the user. The user-system interaction is always in a uniform way, regardless of where and when it takes place. [1] The computers in a distributed s ystem can have a full complement of peripherals, instead of just the basic essential hardware like the multicomputers. [2] There are other goals to distributed systems other than increased computational power. The main goal is to make C

Privacy Clouds

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