13,686 research outputs found
The Capacity of Single-Server Weakly-Private Information Retrieval
A private information retrieval (PIR) protocol guarantees that a user can
privately retrieve files stored in a database without revealing any information
about the identity of the requested file. Existing information-theoretic PIR
protocols ensure perfect privacy, i.e., zero information leakage to the servers
storing the database, but at the cost of high download. In this work, we
present weakly-private information retrieval (WPIR) schemes that trade off
perfect privacy to improve the download cost when the database is stored on a
single server. We study the tradeoff between the download cost and information
leakage in terms of mutual information (MI) and maximal leakage (MaxL) privacy
metrics. By relating the WPIR problem to rate-distortion theory, the
download-leakage function, which is defined as the minimum required download
cost of all single-server WPIR schemes for a given level of information leakage
and a fixed file size, is introduced. By characterizing the download-leakage
function for the MI and MaxL metrics, the capacity of single-server WPIR is
fully described.Comment: To appear in IEEE Journal of Selected Areas in Information Theory
(JSAIT), Special Issue on Privacy and Security of Information Systems, 202
SoK: Cryptographically Protected Database Search
Protected database search systems cryptographically isolate the roles of
reading from, writing to, and administering the database. This separation
limits unnecessary administrator access and protects data in the case of system
breaches. Since protected search was introduced in 2000, the area has grown
rapidly; systems are offered by academia, start-ups, and established companies.
However, there is no best protected search system or set of techniques.
Design of such systems is a balancing act between security, functionality,
performance, and usability. This challenge is made more difficult by ongoing
database specialization, as some users will want the functionality of SQL,
NoSQL, or NewSQL databases. This database evolution will continue, and the
protected search community should be able to quickly provide functionality
consistent with newly invented databases.
At the same time, the community must accurately and clearly characterize the
tradeoffs between different approaches. To address these challenges, we provide
the following contributions:
1) An identification of the important primitive operations across database
paradigms. We find there are a small number of base operations that can be used
and combined to support a large number of database paradigms.
2) An evaluation of the current state of protected search systems in
implementing these base operations. This evaluation describes the main
approaches and tradeoffs for each base operation. Furthermore, it puts
protected search in the context of unprotected search, identifying key gaps in
functionality.
3) An analysis of attacks against protected search for different base
queries.
4) A roadmap and tools for transforming a protected search system into a
protected database, including an open-source performance evaluation platform
and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac
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Emerging Trustworthiness Issues in Distributed Learning Systems
A distributed learning system allocates learning processes onto several workstations to enable faster learning algorithms. Federated Learning (FL) is an increasingly popular type of distributed learning which allows mutually untrusted clients to collaboratively train a common machine learning model without sharing their private/proprietary training data with each other. In this dissertation, we aim to address emerging trustworthiness issues in distributed learning systems, particularly in the field of FL.
First, we tackle the issue of robustness in FL and demonstrate its susceptibility by presenting a comprehensive analysis of the various poisoning attacks and defensive aggregation rules proposed in the literature and connecting them under a common framework. To address this issue, we propose Federated Rank Learning (FRL) which reduces the space of client updates from a continuous space of float numbers in standard FL to a discrete space of integer values, limiting the adversary\u27s options for poisoning attacks.
Next, we address the privacy concerns in FL, including access privacy and data privacy. An adversarial server in FL gets information about the data distribution of a target client by monitoring either I) local updates that the target submits throughout the FL training or II) the access pattern of the target, which can be privacy sensitive in many real-world scenarios. To preserve access privacy, we design Heterogeneous Private Information Retrieval (HPIR), which allows clients to fetch their specific model parameters from untrusted servers without leaking any information. We believe that HPIR will enable new application scenarios for private distributed learning systems, as well as improve the usability of some of the known applications of PIR. To preserve data privacy, we show that local rankings leak less information about private training data. We conduct a comprehensive investigation on the privacy of rankings in FRL to measure data leakage compared to weight parameter updates in standard FL in presence of the state-of-the-art white-box membership inference attack.
Finally, we address the issue of fairness in FL where a single model cannot represent all clients equally due to heterogeneity in their data distributions. To alleviate this issue, we propose Equal and Equitable Federated Learning (E2FL). E2FL produces fair federated learning models by preserving both equity and equality among the participating clients based on learning on parameter rankings where multiple global models are learned so that each group of clients can benefit from their personalized model
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