307 research outputs found
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
The -Nearest Neighbor Search (-NNS) is the backbone of several
cloud-based services such as recommender systems, face recognition, and
database search on text and images. In these services, the client sends the
query to the cloud server and receives the response in which case the query and
response are revealed to the service provider. Such data disclosures are
unacceptable in several scenarios due to the sensitivity of data and/or privacy
laws.
In this paper, we introduce SANNS, a system for secure -NNS that keeps
client's query and the search result confidential. SANNS comprises two
protocols: an optimized linear scan and a protocol based on a novel sublinear
time clustering-based algorithm. We prove the security of both protocols in the
standard semi-honest model. The protocols are built upon several
state-of-the-art cryptographic primitives such as lattice-based additively
homomorphic encryption, distributed oblivious RAM, and garbled circuits. We
provide several contributions to each of these primitives which are applicable
to other secure computation tasks. Both of our protocols rely on a new circuit
for the approximate top- selection from numbers that is built from comparators.
We have implemented our proposed system and performed extensive experimental
results on four datasets in two different computation environments,
demonstrating more than faster response time compared to
optimally implemented protocols from the prior work. Moreover, SANNS is the
first work that scales to the database of 10 million entries, pushing the limit
by more than two orders of magnitude.Comment: 18 pages, to appear at USENIX Security Symposium 202
Round Optimal Secure Multiparty Computation from Minimal Assumptions
We construct a four round secure multiparty computation (MPC) protocol in the plain model that achieves security against any dishonest majority. The security of our protocol relies only on the existence of four round oblivious transfer. This culminates the long line of research on constructing round-efficient MPC from minimal assumptions (at least w.r.t. black-box simulation)
Input Secrecy & Output Privacy: Efficient Secure Computation of Differential Privacy Mechanisms
Data is the driving force of modern businesses. For example, customer-generated data is collected by companies to improve their products, discover emerging trends, and provide insights to marketers. However, data might contain personal information which allows to identify a person and violate their privacy. Examples of privacy violations are abundant – such as revealing typical whereabout and habits, financial status, or health information, either directly or indirectly by linking the data to other available data sources. To protect personal data and regulate its collection and processing, the general data protection regulation (GDPR) was adopted by all members of the European Union.
Anonymization addresses such regulations and alleviates privacy concerns by altering personal data to hinder identification. Differential privacy (DP), a rigorous privacy notion for anonymization mechanisms, is widely deployed in the industry, e.g., by Google, Apple, and Microsoft.
Additionally, cryptographic tools, namely, secure multi-party computation (MPC), protect the data during processing. MPC allows distributed parties to jointly compute a function over their data such that only the function output is revealed but none of the input data. MPC and DP provide orthogonal protection guarantees. MPC provides input secrecy, i.e., MPC protects the inputs of a computation via encrypted processing. DP provides output privacy, i.e., DP anonymizes the output of a computation via randomization. In typical deployments of DP the data is randomized locally, i.e., by each client, and aggregated centrally by a server. MPC allows to apply the randomization centrally as well, i.e., only once, which is optimal for accuracy. Overall, MPC and DP augment each other nicely. However, universal MPC is inefficient – requiring large computation and communication overhead – which makes MPC of DP mechanisms challenging for general real-world deployments.
In this thesis, we present efficient MPC protocols for distributed parties to collaboratively compute DP statistics with high accuracy. We support general rank-based statistics, e.g., min, max, median, as well as decomposable aggregate functions, where local evaluations can be efficiently combined to global ones, e.g., for convex optimizations. Furthermore, we detect heavy hitters, i.e., most frequently appearing values, over known as well as unknown data domains. We prove the semi-honest security and differential privacy of our protocols. Also, we theoretically analyse and empirically evaluate their accuracy as well as efficiency. Our protocols provide higher accuracy than comparable solutions based on DP alone. Our protocols are efficient, with running times of seconds to minutes evaluated in real-world WANs between Frankfurt and Ohio (100 ms delay, 100 Mbits/s bandwidth), and have modest hardware requirements compared to related work (mainly, 4 CPU cores at 3.3 GHz and 2 GB RAM per party). Additionally, our protocols can be outsourced, i.e., clients can send encrypted inputs to few servers which run the MPC protocol on their behalf
Information-Theoretic Secure Outsourced Computation in Distributed Systems
Secure multi-party computation (secure MPC) has been established as the de facto paradigm for protecting privacy in distributed computation. One of the earliest secure MPC primitives is the Shamir\u27s secret sharing (SSS) scheme. SSS has many advantages over other popular secure MPC primitives like garbled circuits (GC) -- it provides information-theoretic security guarantee, requires no complex long-integer operations, and often leads to more efficient protocols. Nonetheless, SSS receives less attention in the signal processing community because SSS requires a larger number of honest participants, making it prone to collusion attacks. In this dissertation, I propose an agent-based computing framework using SSS to protect privacy in distributed signal processing. There are three main contributions to this dissertation. First, the proposed computing framework is shown to be significantly more efficient than GC. Second, a novel game-theoretical framework is proposed to analyze different types of collusion attacks. Third, using the proposed game-theoretical framework, specific mechanism designs are developed to deter collusion attacks in a fully distributed manner. Specifically, for a collusion attack with known detectors, I analyze it as games between secret owners and show that the attack can be effectively deterred by an explicit retaliation mechanism. For a general attack without detectors, I expand the scope of the game to include the computing agents and provide deterrence through deceptive collusion requests. The correctness and privacy of the protocols are proved under a covert adversarial model. Our experimental results demonstrate the efficiency of SSS-based protocols and the validity of our mechanism design
Libertas: Privacy-Preserving Computation for Decentralised Personal Data Stores
Data-driven decision-making and AI applications present exciting new
opportunities delivering widespread benefits. The rapid adoption of such
applications triggers legitimate concerns about loss of privacy and misuse of
personal data. This leads to a growing and pervasive tension between harvesting
ubiquitous data on the Web and the need to protect individuals. Decentralised
personal data stores (PDS) such as Solid are frameworks designed to give
individuals ultimate control over their personal data. But current PDS
approaches have limited support for ensuring privacy when computations combine
data spread across users. Secure Multi-Party Computation (MPC) is a well-known
subfield of cryptography, enabling multiple autonomous parties to
collaboratively compute a function while ensuring the secrecy of inputs (input
privacy). These two technologies complement each other, but existing practices
fall short in addressing the requirements and challenges of introducing MPC in
a PDS environment. For the first time, we propose a modular design for
integrating MPC with Solid while respecting the requirements of
decentralisation in this context. Our architecture, Libertas, requires no
protocol level changes in the underlying design of Solid, and can be adapted to
other PDS. We further show how this can be combined with existing differential
privacy techniques to also ensure output privacy. We use empirical benchmarks
to inform and evaluate our implementation and design choices. We show the
technical feasibility and scalability pattern of the proposed system in two
novel scenarios -- 1) empowering gig workers with aggregate computations on
their earnings data; and 2) generating high-quality differentially-private
synthetic data without requiring a trusted centre. With this, we demonstrate
the linear scalability of Libertas, and gained insights about compute
optimisations under such an architecture
QUOTIENT: Two-Party Secure Neural Network Training and Prediction
Recently, there has been a wealth of effort devoted to the design of secure
protocols for machine learning tasks. Much of this is aimed at enabling secure
prediction from highly-accurate Deep Neural Networks (DNNs). However, as DNNs
are trained on data, a key question is how such models can be also trained
securely. The few prior works on secure DNN training have focused either on
designing custom protocols for existing training algorithms, or on developing
tailored training algorithms and then applying generic secure protocols. In
this work, we investigate the advantages of designing training algorithms
alongside a novel secure protocol, incorporating optimizations on both fronts.
We present QUOTIENT, a new method for discretized training of DNNs, along with
a customized secure two-party protocol for it. QUOTIENT incorporates key
components of state-of-the-art DNN training such as layer normalization and
adaptive gradient methods, and improves upon the state-of-the-art in DNN
training in two-party computation. Compared to prior work, we obtain an
improvement of 50X in WAN time and 6% in absolute accuracy
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