Results 1 - 10
of
15
Efficient and privacy-aware data aggregation in mobile sensing
- IEEE Trans. on Dependable and Secure Computing
, 2014
"... Abstract—The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple ..."
Abstract
-
Cited by 5 (2 self)
- Add to MetaCart
(Show Context)
Abstract—The proliferation and ever-increasing capabilities of mobile devices such as smart phones give rise to a variety of mobile sensing applications. This paper studies how an untrusted aggregator in mobile sensing can periodically obtain desired statistics over the data contributed by multiple mobile users, without compromising the privacy of each user. Although there are some existing works in this area, they either require bidirectional communications between the aggregator and mobile users in every aggregation period, or have high computation overhead and cannot support large plaintext spaces. Also, they do not consider the Min aggregate which is quite useful in mobile sensing. To address these problems, we propose an efficient protocol to obtain the Sum aggregate, which employs an additive homomorphic encryption and a novel key management technique to support large plaintext space. We also extend the sum aggrega-tion protocol to obtain the Min aggregate of time-series data. To deal with dynamic joins and leaves of mobile users, we propose a scheme which utilizes the redundancy in security to reduce the communication cost for each join and leave. Evaluations show that our protocols are orders of magnitude faster than existing solutions, and it has much lower communication overhead. Index Terms—Mobile sensing, privacy, data aggregation I.
Implementation of Privacy-Friendly Aggregation for the Smart Grid
"... Abstract. In recent years a number of protocols have been suggested towards privacy-preserving aggregation of smart meter data, allowing electricity network operators to perform a large part of grid maintenance and administrative operations without having to touch any privacy-sensitive data. In ligh ..."
Abstract
-
Cited by 3 (0 self)
- Add to MetaCart
Abstract. In recent years a number of protocols have been suggested towards privacy-preserving aggregation of smart meter data, allowing electricity network operators to perform a large part of grid maintenance and administrative operations without having to touch any privacy-sensitive data. In light of upcoming European legislation, this approach has gained quite some attention. However, to allow such protocols to have a chance to make it into a real system, it is vital to add credibility by demonstrating that the approach scales, is reasonably robust, and can be integrated into the existing and planned smart metering chains. This paper presents results from integration and scalability tests performed on 100 DLMS/COSEM smart meters in collaboration with a meter manufacturer and a Dutch utility. We outline the lessons learned and choices that had to be made to allow the protocols to run in a real system, as well as some privacy challenges that cannot be covered by this technology. 1
Private and Dynamic Time-Series Data Aggregation with Trust Relaxation
"... Abstract. With the advent of networking applications collecting user data on a massive scale, the privacy of individual users appears to be a major concern. The main challenge is the design of a solution that allows the data analyzer to compute global statistics over the set of individual inputs tha ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
(Show Context)
Abstract. With the advent of networking applications collecting user data on a massive scale, the privacy of individual users appears to be a major concern. The main challenge is the design of a solution that allows the data analyzer to compute global statistics over the set of individual inputs that are protected by some confidentiality mechanism. Joye et al. [7] recently suggested a solution that allows a centralized party to compute the sum of encrypted inputs collected through a smart metering network. The main shortcomings of this solution are its reliance on a trusted dealer for key distribution and the need for frequent key updates. In this paper we introduce a secure protocol for aggregation of time-series data that is based on the Joye et al. [7] scheme and in which the main shortcomings of the latter, namely, the requirement for key updates and for the trusted dealer are eliminated. Moreover our scheme supports a dynamic group management, whereby as opposed to Joye et al. [7] leave and join operations do not trigger a key update at the users.
A Privacy-Enhancing Protocol that Provides In-Network Data Aggregation and Verifiable Smart Meter Billing
"... We present an innovative protocol combining in-network data aggregation and smart meter billing for a smart grid scenario. The former enables an energy supplier to allocate and balance resources. The latter provides dynamic pricing schemes according to fine-grained consumption profiles. More-over, ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
We present an innovative protocol combining in-network data aggregation and smart meter billing for a smart grid scenario. The former enables an energy supplier to allocate and balance resources. The latter provides dynamic pricing schemes according to fine-grained consumption profiles. More-over, smart meters and their energy supplier can prove their billing values. Since the energy supplier knows the amount of generated electricity and the consolidated consumption in a round of measurements, the energy supplier can detect energy loss and fraud. To preserve customers ’ privacy, we use a homomorphic commitment scheme with a homomorphic encryption scheme. All data sent from a meter to any other component in the communication network is either a commitment or an encrypted message. To provide security and privacy, we only require software modifications, leaving the hardware of the smart grid unchanged.
What’s the Gist? Privacy-Preserving Aggregation of User Profiles
"... Abstract. Online service providers gather increasingly large amounts of personal data into user profiles and mon-etize them with advertisers and data brokers. Users have little control of what information is processed and face an all-or-nothing decision between receiving free services or refusing to ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
(Show Context)
Abstract. Online service providers gather increasingly large amounts of personal data into user profiles and mon-etize them with advertisers and data brokers. Users have little control of what information is processed and face an all-or-nothing decision between receiving free services or refusing to be profiled. This paper explores an alternative approach where users only disclose an aggregate model – the “gist ” – of their data. The goal is to preserve data utility and simultaneously provide user privacy. We show that this approach is practical and can be realized by let-ting users contribute encrypted and differentially-private data to an aggregator. The aggregator combines encrypted contributions and can only extract an aggregate model of the underlying data. In order to dynamically assess the value of data aggregates, we use an information-theoretic measure to compute the amount of “valuable ” information provided to advertisers and data brokers. We evaluate our framework on an anonymous dataset of 100,000 U.S. users obtained from the U.S. Census Bureau and show that (i) it provides accurate aggregates with as little as 100 users, (ii) it generates revenue for both users and data brokers, and (iii) its overhead is appreciably low. 1
Formal Analysis of a Privacy-Preserving Billing Protocol
"... Abstract. We provide a formal model and a security analysis of the Private Billing Protocol. This formal analysis allowed us to spell out precisely the details of the protocol, the security assumptions as well as the expected security goals. For the formal analysis we used SATMC, a model checker for ..."
Abstract
- Add to MetaCart
Abstract. We provide a formal model and a security analysis of the Private Billing Protocol. This formal analysis allowed us to spell out precisely the details of the protocol, the security assumptions as well as the expected security goals. For the formal analysis we used SATMC, a model checker for security protocol analysis that supports the specifica-tion of security assumptions and goals as LTL formulae. Further analysis that we conducted manually revealed that the protocol allows for imple-mentations that fail to meet the expected privacy goal. We describe the implications of our findings and discuss how the problem can be avoided.
AgSec: Secure and Efficient CDMA-based Aggregation for Smart Metering Systems
"... Abstract-Security and privacy concerns in the future power grid have recently received tremendous focus from security advocates. Most existing security mechanisms utilize cryptographic techniques that are computationally expensive and bandwidth intensive. However, aggregating the large outputs of t ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract-Security and privacy concerns in the future power grid have recently received tremendous focus from security advocates. Most existing security mechanisms utilize cryptographic techniques that are computationally expensive and bandwidth intensive. However, aggregating the large outputs of these cryptographic algorithms has not been considered thoroughly. Smart Grid Networks (SGN) generally have limitations on bandwidth, network capacity and energy. Hence, utilizing data aggregation algorithms, the limited bandwidth can be efficiently utilized. Most of the aggregation algorithms use statistical functions such as minimum, maximum, and average. before transmitting data over the network. Existing aggregation algorithms, in SGNs, are generally expensive in terms of communication overhead, processing load and delay. However, our proposed CDMA-based data aggregation method provides access to all the data of all the smart meters in the root node, which in this case is the Utility Center, while keeping the smart metering data secure. The efficiency of the proposed method is confirmed by mathematical analysis.1
A Privacy-Preserving Scheme for Incentive-Based Demand Response in the Smart Grid
"... ... smart grid provides real-time information to both grid operators and customers, exploiting the full potential of demand response. However, it introduces new privacy threats to customers. Prior works have proposed privacy-preserving methods in the AMI such as temporal or spatial aggregation. A ma ..."
Abstract
- Add to MetaCart
... smart grid provides real-time information to both grid operators and customers, exploiting the full potential of demand response. However, it introduces new privacy threats to customers. Prior works have proposed privacy-preserving methods in the AMI such as temporal or spatial aggregation. A main assumption in these works is that fine-grained data do not need to be attributable to individuals. However, this assumption does not hold in incentive-based demand response (IDR) programs where fine-grained metering data are required to analyze individual demand curtailments and hence need to be attributable. In this paper, we propose a privacy-preserving scheme for IDR programs in the smart grid, which enables the demand response provider (DRP) to compute individual demand curtailments and demand response rewards while preserving customer privacy. Moreover, a customer can reveal his/her identity and prove ownership of his/her power usage profile in certain situations such as legal disputes. We achieve both privacy and efficiency in our scheme through a combination of several cryptographic primitives such as identity-committable signatures (ICS) and partially blind signatures. As far as we know, we are the first to identify and address privacy issues for IDR programs in the smart grid.
Formal Analysis of a Privacy-Preserving Billing Protocol
, 2013
"... We provide a formal model and a security analysis of the Private Billing Protocol. This formal analysis allowed us to spell out precisely the details of the protocol, the security assumptions as well as the expected security goals. For the formal analysis we used SATMC, a model checker for securit ..."
Abstract
- Add to MetaCart
We provide a formal model and a security analysis of the Private Billing Protocol. This formal analysis allowed us to spell out precisely the details of the protocol, the security assumptions as well as the expected security goals. For the formal analysis we used SATMC, a model checker for security protocol analysis that supports the specification of security assumptions and goals as LTL formulae. Further analysis that we conducted manually revealed that the protocol allows for imple-mentations that fail to meet the expected privacy goal. We describe the implications of our findings and discuss how the problem can be avoided.
