Privacy-Preserving Aggregation of Time-Series Data

The conference paper can be viewed at: http://www.isoc.org/isoc/conferences/ndss/11/proceedings.shtmlSession 9: PrivacyWe consider how an untrusted data aggregator can learn desired statistics over multiple participants’ data, without compromising each individual’s privacy. We propose a construction that allows a group of participants to periodically upload encrypted values to a data aggregator, such that the aggregator is able to compute the sum of all participants’ values in every time period, but is unable to learn anything else. We achieve strong privacy guarantees using two main techniques. First, we show how to utilize applied cryptographic techniques to allow the aggregator to decrypt the sum from multiple ciphertexts encrypted under different user keys. Second, we describe a distributed data randomization procedure that guarantees the differential privacy of the outcome statistic, even when a subset of participants might be compromised.published_or_final_versio

Privacy-Preserving Aggregation of Time-Series Data

The conference paper can be viewed at: http://www.isoc.org/isoc/conferences/ndss/11/proceedings.shtmlSession 9: PrivacyWe consider how an untrusted data aggregator can learn desired statistics over multiple participants’ data, without compromising each individual’s privacy. We propose a construction that allows a group of participants to periodically upload encrypted values to a data aggregator, such that the aggregator is able to compute the sum of all participants’ values in every time period, but is unable to learn anything else. We achieve strong privacy guarantees using two main techniques. First, we show how to utilize applied cryptographic techniques to allow the aggregator to decrypt the sum from multiple ciphertexts encrypted under different user keys. Second, we describe a distributed data randomization procedure that guarantees the differential privacy of the outcome statistic, even when a subset of participants might be compromised.published_or_final_versio

A New Framework for Privacy-Preserving Aggregation of Time-Series Data

International audienceAggregator-oblivious encryption is a useful notion put forward by Shi et al. in 2011 that allows an untrusted aggregator to periodically compute an aggregate value over encrypted data contributed by a set of users. Such encryption schemes find numerous applications, in particular in the context of privacy-preserving smart metering.This paper presents a general framework for constructing privacy-preserving aggregator-oblivious encryption schemes using a variant of Cramer-Shoup's paradigm of smooth projective hashing. This abstraction leads to new schemes based on a variety of complexity assumptions. It also improves upon existing constructions, providing schemes with shorter ciphertexts and better encryption times

Privacy-Friendly Mobility Analytics using Aggregate Location Data

Location data can be extremely useful to study commuting patterns and disruptions, as well as to predict real-time traffic volumes. At the same time, however, the fine-grained collection of user locations raises serious privacy concerns, as this can reveal sensitive information about the users, such as, life style, political and religious inclinations, or even identities. In this paper, we study the feasibility of crowd-sourced mobility analytics over aggregate location information: users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates -- i.e., how many, but not which, users are in a region at a given time. We experiment with real-world mobility datasets obtained from the Transport For London authority and the San Francisco Cabs network, and present a novel methodology based on time series modeling that is geared to forecast traffic volumes in regions of interest and to detect mobility anomalies in them. In the presence of anomalies, we also make enhanced traffic volume predictions by feeding our model with additional information from correlated regions. Finally, we present and evaluate a mobile app prototype, called Mobility Data Donors (MDD), in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201

Privacy-Preserving Aggregation of Time-Series Data with Public Verifiability from Simple Assumptions

Aggregator oblivious encryption was proposed by Shi et al. (NDSS 2011), where an aggregator can compute an aggregated sum of data and is unable to learn anything else (aggregator obliviousness). Since the aggregator does not learn individual data that may reveal users\u27 habits and behaviors, several applications, such as privacy-preserving smart metering, have been considered. In this paper, we propose aggregator oblivious encryption schemes with public verifiability where the aggregator is required to generate a proof of an aggregated sum and anyone can verify whether the aggregated sum has been correctly computed by the aggregator. Though Leontiadis et al. (CANS 2015) considered the verifiability, their scheme requires an interactive complexity assumption to provide the unforgeability of the proof. Our schemes are proven to be unforgeable under a static and simple assumption (a variant of the Computational Diffie-Hellman assumption). Moreover, our schemes inherit the tightness of the reduction of the Benhamouda et al. scheme (ACM TISSEC 2016) for proving aggregator obliviousness. This tight reduction allows us to employ elliptic curves of a smaller order and leads to efficient implementation

TERSE: Tiny Encryptions and Really Speedy Execution for Post-Quantum Private Stream Aggregation

The massive scale and performance demands of privacy-preserving data aggregation make integration of security and privacy difficult. Traditional tools in private computing are not well-suited to handle these challenges, especially for more limited client devices. Efficient primitives and protocols for secure and private data aggregation are a promising approach for private data analytics with resource-constrained devices. However, even such efficient primitives may be much slower than computation with plain data (i.e., without security/privacy guarantees). In this paper, we present TERSE, a new Private Stream Aggregation (PSA) protocol for quantum-secure time-series additive data aggregation. Due to its simplicity, low latency, and low communication overhead, TERSE is uniquely well-suited for real-world deployment. In our implementation, TERSE shows very low latency for both clients and servers, achieving encryption latency on a smartphone of 0.0003 ms and aggregation latency of 0.006 ms for 1000 users. TERSE also shows significant improvements in latency over other state-of-the-art quantum-secure PSA, achieving improvements of 1796x to 12406x for encryption at the client\u27s end and 848x to 5433x for aggregation and decryption at the server\u27s end

A novel temporal perturbation based privacy-preserving scheme for real-time monitoring systems

In real-time monitoring systems, participant's privacy could be easily exposed when the time-series of sensing measurements are obtained accurately by adversaries. To address privacy issues, a number of privacy-preserving schemes have been designed for various monitoring applications. However, these schemes either lack considerations for temporal privacy or have less resistance to filtering attacks, or cause time delay with low utility. In this paper, we introduce a lightweight temporal perturbation based scheme, where sensor readings are buffered and disordered to obfuscate the temporal information of the original sensor measurement stream with differential privacy. Besides, we design the operations on the system server side to exploit the data utility in measurements from large number of sensors. We evaluate the performance of the proposed scheme through both rigorous theoretical analysis and extensive simulation experiments in comparison with related existing schemes. Evaluation results show that the proposed scheme manages to preserve both the temporal privacy and measurement privacy with filter-resistance, and achieves better performance in terms of computational overhead, data utility of real-time aggregation, and individual accumulation

A novel temporal perturbation based privacy-preserving scheme for real-time monitoring systems

In real-time monitoring systems, participant’s privacy could be easily exposed when the time-series of sensing measurements are obtained accurately by adversaries. To address privacy issues, a number of privacy-preserving schemes have been designed for various monitoring applications. However, these schemes either lack considerations for temporal privacy or have less resistance to filtering attacks, or cause time delay with low utility. In this paper, we introduce a lightweight temporal perturbation based scheme, where sensor readings are buffered and disordered to obfuscate the temporal information of the original sensor measurement stream with differential privacy. Besides, we design the operations on the system server side to exploit the data utility in measurements from large number of sensors. We evaluate the performance of the proposed scheme through both rigorous theoretical analysis and extensive simulation experiments in comparison with related existing schemes. Evaluation results show that the proposed scheme manages to preserve both the temporal privacy and measurement privacy with filter-resistance, and achieves better performance in terms of computational overhead, data utility of real-time aggregation, and individual accumulation