Privacy and Data Protection Act 2014

This report establishes a data security regime for all information held by the Victorian public sector. Authorised Version No. 001 - Privacy and Data Protection Act 2014 - No. 60 of 2014 Authorised Version incorporating amendments as at 17 September 2014 The Parliament of Victoria enacts: PART 1—PRELIMINARY 1 Purposes The purposes of this Act are— (a) to provide for responsible collection and handling of personal information in the Victorian public sector; and (b) to provide remedies for interferences with the information privacy of an individual; and (c) to establish a protective data security regime for the Victorian public sector; and (d) to establish a regime for monitoring and assuring public sector data security; and (e) to establish the Commissioner for Privacy and Data Protection; and (f) to repeal the Information Privacy Act 2000 and the Commissioner for Law Enforcement Data Security Act 2005 and make consequential amendments to other Acts

Secret charing vs. encryption-based techniques for privacy preserving data mining

Privacy preserving querying and data publishing has been studied in the context of statistical databases and statistical disclosure control. Recently, large-scale data collection and integration efforts increased privacy concerns which motivated data mining researchers to investigate privacy implications of data mining and how data mining can be performed without violating privacy. In this paper, we first provide an overview of privacy preserving data mining focusing on distributed data sources, then we compare two technologies used in privacy preserving data mining. The first technology is encryption based, and it is used in earlier approaches. The second technology is secret-sharing which is recently being considered as a more efficient approach

Privacy-enhancing Aggregation of Internet of Things Data via Sensors Grouping

Big data collection practices using Internet of Things (IoT) pervasive technologies are often privacy-intrusive and result in surveillance, profiling, and discriminatory actions over citizens that in turn undermine the participation of citizens to the development of sustainable smart cities. Nevertheless, real-time data analytics and aggregate information from IoT devices open up tremendous opportunities for managing smart city infrastructures. The privacy-enhancing aggregation of distributed sensor data, such as residential energy consumption or traffic information, is the research focus of this paper. Citizens have the option to choose their privacy level by reducing the quality of the shared data at a cost of a lower accuracy in data analytics services. A baseline scenario is considered in which IoT sensor data are shared directly with an untrustworthy central aggregator. A grouping mechanism is introduced that improves privacy by sharing data aggregated first at a group level compared as opposed to sharing data directly to the central aggregator. Group-level aggregation obfuscates sensor data of individuals, in a similar fashion as differential privacy and homomorphic encryption schemes, thus inference of privacy-sensitive information from single sensors becomes computationally harder compared to the baseline scenario. The proposed system is evaluated using real-world data from two smart city pilot projects. Privacy under grouping increases, while preserving the accuracy of the baseline scenario. Intra-group influences of privacy by one group member on the other ones are measured and fairness on privacy is found to be maximized between group members with similar privacy choices. Several grouping strategies are compared. Grouping by proximity of privacy choices provides the highest privacy gains. The implications of the strategy on the design of incentives mechanisms are discussed