Abstract-Smart metering systems provide high resolution, realtime end user power consumption data for utilities to better monitor and control the system, and for end users to better manage their energy usage and bills. However, the high resolution realtime power consumption data can also be used to extract end user activity details, which could pose a great threat to user privacy. In this work, we propose a secure multi-party computation (SMC) based privacy preserving protocol for smart meter based load management. Using SMC and a proper designed electricity plan, the utility is able to perform real time demand management with individual users, without knowing the actual value of each user's consumption data. Using homomorphic encryption, the billing is secure and verifiable. We have further implemented a demonstration system which includes a graphical user interface and simulates network communication. The demonstration shows that the proposed privacy preserving protocol is feasible for implementation on commodity IT systems.

Traditional distributed Data Stream Management Systems assign query operators to sites by optimizing for some criterion such as query throughput, or network delay. The work presented in this paper begins to augment this traditional operator placement technique by allowing the user issuing a continuous query to specify a variety of constraints-including collocation, upstream/downstream, and tag-or attribute-based constraints-controlling operator placement within the query network. Given a set of constraints, operators, and sites; four strategies are presented for optimizing the operator placement. An optimal brute force algorithm is presented first for smaller cases, followed by linear programming, constraint satisfaction, and local search strategies. The four methods are compared for speed, accuracy, and efficiency, with constraint satisfaction performing the best, and allowing assignments to be adapted on the fly by the DDSMS.

Abstract-Smart metering systems in distribution networks provide near real-time, two-way information exchange between end users and utilities, enabling many advanced smart grid technologies. However, the fine grained real-time data as well as the various market functionalities also pose great risks to customer privacy. In this work we propose a secure multi-party computation (SMC) based privacy preserving smart metering system. Using the proposed SMC protocol, a utility is able to perform advanced market based demand management algorithms without knowing the actual values of private end user consumption and configuration data. Using homomorphic encryption, billing is secure and verifiable. We implemented a demonstration system that includes a graphical user interface and simulates realworld network communication of the proposed SMC-enabled smart meters. The demonstration shows the feasibility of our proposed privacy preserving protocol for advanced smart grid technologies which includes load management and retail level electricity market support.

With data becoming available in larger quantities and at higher rates, new data processing paradigms have been proposed to handle high-volume, fast-moving data. Data Stream Processing is one such paradigm wherein transient data streams flow through sets of continuous queries, only returning results when data is of interest to the querier. To avoid the large costs associated with maintaining the infrastructure required for processing these data streams, many companies will outsource their computation to third-party cloud services. This outsourcing, however, can lead to private data being accessed by parties that a data provider may not trust. The literature offers solutions to this confidentiality and access control problem but they have fallen short of providing a complete solution to these problems, due to either immense overheads or trust requirements placed on these third-party services. To address these issues, we have developed PolyStream, an enhancement to existing data stream management systems that enables data providers to specify attribute-based access control policies that are cryptographically enforced while simultaneously allowing many types of in-network data processing. We detail the access control models and mechanisms used by PolyStream, and describe a novel use of security punctuations that enables flexible, online policy management and key distribution. We detail how queries are submitted and executed using an unmodified Data Stream Management System, and show through an extensive evaluation that PolyStream yields a 550x performance gain versus the state-of-the-art system StreamForce in CODASPY 2014, while providing greater functionality to the querier.

Data Stream Management Systems (DSMS) are crucial for modern high-volume/high-velocity data-driven applications, necessitating a distributed approach to processing them. In addition, data providers often require certain levels of confidentiality for their data, especially in cases of user-generated data, such as those coming out of physical activity/health tracking devices (i.e., our motivating application). This demonstration will showcase Synefo, an infrastructure that enables elastic scaling of DSMS operators, and CryptStream, a framework that provides confidentiality and access controls for data streams while allowing computation on untrusted servers, fused as CE-Storm. We will demonstrate both systems working in tandem and also visualize their behavior over time under different scenarios.