286 research outputs found
Hidden in the Cloud : Advanced Cryptographic Techniques for Untrusted Cloud Environments
In the contemporary digital age, the ability to search and perform operations on encrypted data has become increasingly important. This significance is primarily due to the exponential growth of data, often referred to as the "new oil," and the corresponding rise in data privacy concerns. As more and more data is stored in the cloud, the need for robust security measures to protect this data from unauthorized access and misuse has become paramount.
One of the key challenges in this context is the ability to perform meaningful operations on the data while it remains encrypted. Traditional encryption techniques, while providing a high level of security, render the data unusable for any practical purpose other than storage. This is where advanced cryptographic protocols like Symmetric Searchable Encryption (SSE), Functional Encryption (FE), Homomorphic Encryption (HE), and Hybrid Homomorphic Encryption (HHE) come into play. These protocols not only ensure the confidentiality of data but also allow computations on encrypted data, thereby offering a higher level of security and privacy.
The ability to search and perform operations on encrypted data has several practical implications. For instance, it enables efficient Boolean queries on encrypted databases, which is crucial for many "big data" applications. It also allows for the execution of phrase searches, which are important for many machine learning applications, such as intelligent medical data analytics. Moreover, these capabilities are particularly relevant in the context of sensitive data, such as health records or financial information, where the privacy and security of user data are of utmost importance.
Furthermore, these capabilities can help build trust in digital systems. Trust is a critical factor in the adoption and use of digital services. By ensuring the confidentiality, integrity, and availability of data, these protocols can help build user trust in cloud services. This trust, in turn, can drive the wider adoption of digital services, leading to a more inclusive digital society.
However, it is important to note that while these capabilities offer significant advantages, they also present certain challenges. For instance, the computational overhead of these protocols can be substantial, making them less suitable for scenarios where efficiency is a critical requirement. Moreover, these protocols often require sophisticated key management mechanisms, which can be challenging to implement in practice. Therefore, there is a need for ongoing research to address these challenges and make these protocols more efficient and practical for real-world applications.
The research publications included in this thesis offer a deep dive into the intricacies and advancements in the realm of cryptographic protocols, particularly in the context of the challenges and needs highlighted above.
Publication I presents a novel approach to hybrid encryption, combining the strengths of ABE and SSE. This fusion aims to overcome the inherent limitations of both techniques, offering a more secure and efficient solution for key sharing and access control in cloud-based systems. Publication II further expands on SSE, showcasing a dynamic scheme that emphasizes forward and backward privacy, crucial for ensuring data integrity and confidentiality. Publication III and Publication IV delve into the potential of MIFE, demonstrating its applicability in real-world scenarios, such as designing encrypted private databases and additive reputation systems. These publications highlight the transformative potential of MIFE in bridging the gap between theoretical cryptographic concepts and practical applications. Lastly, Publication V underscores the significance of HE and HHE as a foundational element for secure protocols, emphasizing its potential in devices with limited computational capabilities.
In essence, these publications not only validate the importance of searching and performing operations on encrypted data but also provide innovative solutions to the challenges mentioned. They collectively underscore the transformative potential of advanced cryptographic protocols in enhancing data security and privacy, paving the way for a more secure digital future
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Scalable Auction Algorithms for Bipartite Maximum Matching Problems
In this paper, we give new auction algorithms for maximum weighted bipartite
matching (MWM) and maximum cardinality bipartite -matching (MCbM). Our
algorithms run in and rounds, respectively, in the blackboard distributed
setting. We show that our MWM algorithm can be implemented in the distributed,
interactive setting using and bit messages,
respectively, directly answering the open question posed by Demange, Gale and
Sotomayor [DNO14]. Furthermore, we implement our algorithms in a variety of
other models including the the semi-streaming model, the shared-memory
work-depth model, and the massively parallel computation model. Our
semi-streaming MWM algorithm uses passes in space and our MCbM algorithm runs in
passes using space (where parameters represent
the degree constraints on the -matching and and represent the left
and right side of the bipartite graph, respectively). Both of these algorithms
improves \emph{exponentially} the dependence on in the space
complexity in the semi-streaming model against the best-known algorithms for
these problems, in addition to improvements in round complexity for MCbM.
Finally, our algorithms eliminate the large polylogarithmic dependence on
in depth and number of rounds in the work-depth and massively parallel
computation models, respectively, improving on previous results which have
large polylogarithmic dependence on (and exponential dependence on
in the MPC model).Comment: To appear in APPROX 202
Probabilistic Metric Embedding via Metric Labeling
We consider probabilistic embedding of metric spaces into ultra-metrics (or equivalently to a constant factor, into hierarchically separated trees) to minimize the expected distortion of any pairwise distance. Such embeddings have been widely used in network design and online algorithms. Our main result is a polynomial time algorithm that approximates the optimal distortion on any instance to within a constant factor. We achieve this via a novel LP formulation that reduces this problem to a probabilistic version of uniform metric labeling
Hintless Single-Server Private Information Retrieval
We present two new constructions for private information retrieval (PIR) in the classical setting where the clients do not need to do any preprocessing or store any database dependent information, and the server does not need to store any client-dependent information.
Our first construction HintlessPIR eliminates the client preprocessing step from the recent LWE-based SimplePIR (Henzinger et. al., USENIX Security 2023) by outsourcing the hint related computation to the server, leveraging a new concept of homomorphic encryption with composable preprocessing.
We realize this concept on RLWE encryption schemes, and thanks to the composibility of this technique we are able to preprocess almost all the expensive parts of the homomorphic computation and reuse across multiple executions.
As a concrete application, we achieve very efficient matrix vector multiplication that allows us to build HintlessPIR. For a database of size 8GB, HintlessPIR achieves throughput about 3.7GB/s without requiring any client or server state.
We additionally formalize the matrix vector multiplication protocol as LinPIR primitive, which may be of independent interests.
In our second construction TensorPIR we reduce the communications of HintlessPIR from square root to cubic root in the database size.
For this purpose we extend our HE with preprocessing techniques to composition of key-switching keys and the query expansion algorithm.
We show how to use RLWE encryption with preprocessing to outsource LWE decryption for ciphertexts generated by homomorphic multiplications.
This allows the server to do more complex processing using a more compact query under LWE.
We implement and benchmark HintlessPIR which achieves better concrete costs than TensorPIR for a large set of databases of interest.
We show that it improves the communication of recent preprocessing constructions when clients do not have large numbers of queries or database updates frequently.
The computation cost for removing the hint is small and decreases as the database becomes larger, and it is always more efficient than other constructions with client hints such as Spiral PIR (Menon and Wu, S&P 2022).
In the setting of anonymous queries we also improve on Spiral\u27s communication
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
The Predicted-Deletion Dynamic Model: Taking Advantage of ML Predictions, for Free
The main bottleneck in designing efficient dynamic algorithms is the unknown
nature of the update sequence. In particular, there are some problems, like
3-vertex connectivity, planar digraph all pairs shortest paths, and others,
where the separation in runtime between the best partially dynamic solutions
and the best fully dynamic solutions is polynomial, sometimes even exponential.
In this paper, we formulate the predicted-deletion dynamic model, motivated
by a recent line of empirical work about predicting edge updates in dynamic
graphs. In this model, edges are inserted and deleted online, and when an edge
is inserted, it is accompanied by a "prediction" of its deletion time. This
models real world settings where services may have access to historical data or
other information about an input and can subsequently use such information make
predictions about user behavior. The model is also of theoretical interest, as
it interpolates between the partially dynamic and fully dynamic settings, and
provides a natural extension of the algorithms with predictions paradigm to the
dynamic setting.
We give a novel framework for this model that "lifts" partially dynamic
algorithms into the fully dynamic setting with little overhead. We use our
framework to obtain improved efficiency bounds over the state-of-the-art
dynamic algorithms for a variety of problems. In particular, we design
algorithms that have amortized update time that scales with a partially dynamic
algorithm, with high probability, when the predictions are of high quality. On
the flip side, our algorithms do no worse than existing fully-dynamic
algorithms when the predictions are of low quality. Furthermore, our algorithms
exhibit a graceful trade-off between the two cases. Thus, we are able to take
advantage of ML predictions asymptotically "for free.'
Online Matching with Set and Concave Delays
We initiate the study of online problems with set delay, where the delay cost at any given time is an arbitrary function of the set of pending requests. In particular, we study the online min-cost perfect matching with set delay (MPMD-Set) problem, which generalises the online min-cost perfect matching with delay (MPMD) problem introduced by Emek et al. (STOC 2016). In MPMD, m requests arrive over time in a metric space of n points. When a request arrives the algorithm must choose to either match or delay the request. The goal is to create a perfect matching of all requests while minimising the sum of distances between matched requests, and the total delay costs incurred by each of the requests. In contrast to previous work we study MPMD-Set in the non-clairvoyant setting, where the algorithm does not know the future delay costs. We first show no algorithm is competitive in n or m. We then study the natural special case of size-based delay where the delay is a non-decreasing function of the number of unmatched requests. Our main result is the first non-clairvoyant algorithms for online min-cost perfect matching with size-based delay that are competitive in terms of m. In fact, these are the first non-clairvoyant algorithms for any variant of MPMD. A key technical ingredient is an analog of the symmetric difference of matchings that may be useful for other special classes of set delay. Furthermore, we prove a lower bound of ?(n) for any deterministic algorithm and ?(log n) for any randomised algorithm. These lower bounds also hold for clairvoyant algorithms. Finally, we also give an m-competitive deterministic algorithm for uniform concave delays in the clairvoyant setting
Communication-Efficient Topologies for Decentralized Learning with Consensus Rate
Decentralized optimization is an emerging paradigm in distributed learning in
which agents achieve network-wide solutions by peer-to-peer communication
without the central server. Since communication tends to be slower than
computation, when each agent communicates with only a few neighboring agents
per iteration, they can complete iterations faster than with more agents or a
central server. However, the total number of iterations to reach a network-wide
solution is affected by the speed at which the agents' information is ``mixed''
by communication. We found that popular communication topologies either have
large maximum degrees (such as stars and complete graphs) or are ineffective at
mixing information (such as rings and grids). To address this problem, we
propose a new family of topologies, EquiTopo, which has an (almost) constant
degree and a network-size-independent consensus rate that is used to measure
the mixing efficiency.
In the proposed family, EquiStatic has a degree of , where
is the network size, and a series of time-dependent one-peer topologies,
EquiDyn, has a constant degree of 1. We generate EquiDyn through a certain
random sampling procedure. Both of them achieve an -independent consensus
rate. We apply them to decentralized SGD and decentralized gradient tracking
and obtain faster communication and better convergence, theoretically and
empirically. Our code is implemented through BlueFog and available at
\url{https://github.com/kexinjinnn/EquiTopo}Comment: NeurIPS 202
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