2017/10/09 by Thomas Asikis, Evangelos Pournaras, Asikis, Thomas +1
Computer Science · Engineering · #Blockchain Technology Applications and Security #Computers and Society (cs.CY) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Green IT and Sustainability #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1710.03186
openalex publication_date 2017/10/09 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
The pervasiveness of Internet of Things results in vast volumes of personal\ndata generated by smart devices of users (data producers) such as smart phones,\nwearables and other embedded sensors. It is a common requirement, especially\nfor Big Data analytics systems, to transfer these large in scale and\ndistributed data to centralized computational systems for analysis.\nNevertheless, third parties that run and manage these systems (data consumers)\ndo not always guarantee users' privacy. Their primary interest is to improve\nutility that is usually a metric related to the performance, costs and the\nquality of service. There are several techniques that mask user-generated data\nto ensure privacy, e.g. differential privacy. Setting up a process for masking\ndata, referred to in this paper as a `privacy setting', decreases on the one\nhand the utility of data analytics, while, on the other hand, increases\nprivacy. This paper studies parameterizations of privacy-settings that regulate\nthe trade-off between maximum utility, minimum privacy and minimum utility,\nmaximum privacy, where utility refers to the accuracy in the approximations of\naggregation functions. Privacy settings can be universally applied as\nsystem-wide parameterizations and policies (homogeneous data sharing).\nNonetheless they can also be applied autonomously by each user or decided under\nthe influence of (monetary) incentives (heterogeneous data sharing). This\nlatter diversity in data sharing by informational self-determination plays a\nkey role on the privacy-utility trajectories as shown in this paper both\ntheoretically and empirically. A generic and novel computational framework is\nintroduced for measuring privacy-utility trade-offs and their optimization. The\nframework computes a broad spectrum of such trade-offs that form\nprivacy-utility trajectories under homogeneous and heterogeneous data sharing.\n